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In this study, we are testing whether infants (9-15 months) prefer to listen to speech produced by adults or by 5-6 year old children, and whether this differs as a function of whether the speaker is male or female.

Current speech recognition systems tend to be developed only for commercially viable languages. The resources needed for a typical speech recognition system include hundreds of hours of transcribed speech for acoustic models and 10 to 100 million words of text for language models; both of these requirements can be costly in time and money. The goal of this research is to facilitate rapid development of speech systems to new languages by using multilingual phoneme models to alleviate requirements for large amounts of transcribed speech. The Global Phone database, winch contains transcribed speech from 15 languages, is used as source data to derive multilingual phoneme models. Various bootstrapping processes arc used to develop an Arabic speech recognition system starting from monolingual English models, International Phonetic Association (IP based multilingual models, and data-driven multilingual models. The Kullback-Leibler distortion measure is used to derive data-driven phoneme clusters. It was found that multilingual bootstrapping methods outperform monolingual English bootstrapping methods on the Arabic evaluation data initially, and after three iterations of bootstrapping all systems show similar performance levels

Robust speech processing in practical operating environments requires effective environmental and processor noise suppression. This report describes the technical findings and accomplishments during this reporting period for the research program funded to develop real time, compressed speech analysis- synthesis algorithms whose performance is invariant under signal contamination. Fulfillment of this requirement is necessary to insure reliable secure compressed speech transmission within realistic military command and control environments. Overall contributions resulting from this research program include the understanding of how environmental noise degrades narrow band, coded speech, development of appropriate real time noise suppression algorithms, and development of speech parameter identification methods that consider signal contamination as a fundamental element in the estimation process. Through the appropriate integration of developed noise suppression-parameter identification algorithms, specifications for robust speech processing algorithms will be provided.

Robust speech processing in practical operating environments requires effective environmental and processor noise suppression. This report describes the technical findings and accomplishments during this reporting period for the research program funded to develop real time, compressed speech analysis- synthesis algorithms whose performance is invariant under signal contamination. Fulfillment of this requirement is necessary to insure reliable secure compressed speech transmission within realistic military command and control environments. Overall contributions resulting from this research program include the understanding of how environmental noise degrades narrow band, coded speech, development of appropriate real time noise suppression algorithms, and development of speech parameter identification methods that consider signal contamination as a fundamental element in the estimation process. Through the appropriate integration of developed noise suppression-parameter identification algorithms, specifications for robust speech processing algorithms will be provided.

Book Source: Digital Library of India Item 2015.187878 dc.contributor.author: Peeyush Pandey dc.date.accessioned: 2015-07-07T21:29:34Z dc.date.available: 2015-07-07T21:29:34Z dc.date.digitalpublicationdate: 2005-09-27 dc.identifier.barcode: 1990010089590 dc.identifier.origpath: /rawdataupload/upload/0089/590 dc.identifier.copyno: 1 dc.identifier.uri: http://www.new.dli.ernet.in/handle/2015/187878 dc.description.scanningcentre: IIIT, Allahabad dc.description.main: 1 dc.description.tagged: 0 dc.description.totalpages: 117 dc.format.mimetype: application/pdf dc.language.iso: English dc.publisher.digitalrepublisher: Digital Library Of India dc.publisher: I I T Kanpur dc.rights: Out_of_copyright dc.source.library: I I T Kanpur dc.subject.classification: Technology dc.subject.classification: Engineering. Technology In General dc.subject.classification: Electrical Engineering dc.title: Implementation Of Lpc Based Speech Processing Algorithms On Adsp 2100

Book Source: Digital Library of India Item 2015.187878 dc.contributor.author: Peeyush Pandey dc.date.accessioned: 2015-07-07T21:29:34Z dc.date.available: 2015-07-07T21:29:34Z dc.date.digitalpublicationdate: 2005-09-27 dc.identifier.barcode: 1990010089590 dc.identifier.origpath: /rawdataupload/upload/0089/590 dc.identifier.copyno: 1 dc.identifier.uri: http://www.new.dli.ernet.in/handle/2015/187878 dc.description.scanningcentre: IIIT, Allahabad dc.description.main: 1 dc.description.tagged: 0 dc.description.totalpages: 117 dc.format.mimetype: application/pdf dc.language.iso: English dc.publisher.digitalrepublisher: Digital Library Of India dc.publisher: I I T Kanpur dc.rights: Out_of_copyright dc.source.library: I I T Kanpur dc.subject.classification: Technology dc.subject.classification: Engineering. Technology In General dc.subject.classification: Electrical Engineering dc.title: Implementation Of Lpc Based Speech Processing Algorithms On Adsp 2100

Book Source: Digital Library of India Item 2015.187878 dc.contributor.author: Peeyush Pandey dc.date.accessioned: 2015-07-07T21:29:34Z dc.date.available: 2015-07-07T21:29:34Z dc.date.digitalpublicationdate: 2005-09-27 dc.identifier.barcode: 1990010089590 dc.identifier.origpath: /rawdataupload/upload/0089/590 dc.identifier.copyno: 1 dc.identifier.uri: http://www.new.dli.ernet.in/handle/2015/187878 dc.description.scanningcentre: IIIT, Allahabad dc.description.main: 1 dc.description.tagged: 0 dc.description.totalpages: 117 dc.format.mimetype: application/pdf dc.language.iso: English dc.publisher.digitalrepublisher: Digital Library Of India dc.publisher: I I T Kanpur dc.rights: Out_of_copyright dc.source.library: I I T Kanpur dc.subject.classification: Technology dc.subject.classification: Engineering. Technology In General dc.subject.classification: Electrical Engineering dc.title: Implementation Of Lpc Based Speech Processing Algorithms On Adsp 2100

Book Source: Digital Library of India Item 2015.187878 dc.contributor.author: Peeyush Pandey dc.date.accessioned: 2015-07-07T21:29:34Z dc.date.available: 2015-07-07T21:29:34Z dc.date.digitalpublicationdate: 2005-09-27 dc.identifier.barcode: 1990010089590 dc.identifier.origpath: /rawdataupload/upload/0089/590 dc.identifier.copyno: 1 dc.identifier.uri: http://www.new.dli.ernet.in/handle/2015/187878 dc.description.scanningcentre: IIIT, Allahabad dc.description.main: 1 dc.description.tagged: 0 dc.description.totalpages: 117 dc.format.mimetype: application/pdf dc.language.iso: English dc.publisher.digitalrepublisher: Digital Library Of India dc.publisher: I I T Kanpur dc.rights: Out_of_copyright dc.source.library: I I T Kanpur dc.subject.classification: Technology dc.subject.classification: Engineering. Technology In General dc.subject.classification: Electrical Engineering dc.title: Implementation Of Lpc Based Speech Processing Algorithms On Adsp 2100

Book Source: Digital Library of India Item 2015.187878 dc.contributor.author: Peeyush Pandey dc.date.accessioned: 2015-07-07T21:29:34Z dc.date.available: 2015-07-07T21:29:34Z dc.date.digitalpublicationdate: 2005-09-27 dc.identifier.barcode: 1990010089590 dc.identifier.origpath: /rawdataupload/upload/0089/590 dc.identifier.copyno: 1 dc.identifier.uri: http://www.new.dli.ernet.in/handle/2015/187878 dc.description.scanningcentre: IIIT, Allahabad dc.description.main: 1 dc.description.tagged: 0 dc.description.totalpages: 117 dc.format.mimetype: application/pdf dc.language.iso: English dc.publisher.digitalrepublisher: Digital Library Of India dc.publisher: I I T Kanpur dc.rights: Out_of_copyright dc.source.library: I I T Kanpur dc.subject.classification: Technology dc.subject.classification: Engineering. Technology In General dc.subject.classification: Electrical Engineering dc.title: Implementation Of Lpc Based Speech Processing Algorithms On Adsp 2100

Book Source: Digital Library of India Item 2015.187878 dc.contributor.author: Peeyush Pandey dc.date.accessioned: 2015-07-07T21:29:34Z dc.date.available: 2015-07-07T21:29:34Z dc.date.digitalpublicationdate: 2005-09-27 dc.identifier.barcode: 1990010089590 dc.identifier.origpath: /rawdataupload/upload/0089/590 dc.identifier.copyno: 1 dc.identifier.uri: http://www.new.dli.ernet.in/handle/2015/187878 dc.description.scanningcentre: IIIT, Allahabad dc.description.main: 1 dc.description.tagged: 0 dc.description.totalpages: 117 dc.format.mimetype: application/pdf dc.language.iso: English dc.publisher.digitalrepublisher: Digital Library Of India dc.publisher: I I T Kanpur dc.rights: Out_of_copyright dc.source.library: I I T Kanpur dc.subject.classification: Technology dc.subject.classification: Engineering. Technology In General dc.subject.classification: Electrical Engineering dc.title: Implementation Of Lpc Based Speech Processing Algorithms On Adsp 2100

Book Source: Digital Library of India Item 2015.187878 dc.contributor.author: Peeyush Pandey dc.date.accessioned: 2015-07-07T21:29:34Z dc.date.available: 2015-07-07T21:29:34Z dc.date.digitalpublicationdate: 2005-09-27 dc.identifier.barcode: 1990010089590 dc.identifier.origpath: /rawdataupload/upload/0089/590 dc.identifier.copyno: 1 dc.identifier.uri: http://www.new.dli.ernet.in/handle/2015/187878 dc.description.scanningcentre: IIIT, Allahabad dc.description.main: 1 dc.description.tagged: 0 dc.description.totalpages: 117 dc.format.mimetype: application/pdf dc.language.iso: English dc.publisher.digitalrepublisher: Digital Library Of India dc.publisher: I I T Kanpur dc.rights: Out_of_copyright dc.source.library: I I T Kanpur dc.subject.classification: Technology dc.subject.classification: Engineering. Technology In General dc.subject.classification: Electrical Engineering dc.title: Implementation Of Lpc Based Speech Processing Algorithms On Adsp 2100

Book Source: Digital Library of India Item 2015.187878 dc.contributor.author: Peeyush Pandey dc.date.accessioned: 2015-07-07T21:29:34Z dc.date.available: 2015-07-07T21:29:34Z dc.date.digitalpublicationdate: 2005-09-27 dc.identifier.barcode: 1990010089590 dc.identifier.origpath: /rawdataupload/upload/0089/590 dc.identifier.copyno: 1 dc.identifier.uri: http://www.new.dli.ernet.in/handle/2015/187878 dc.description.scanningcentre: IIIT, Allahabad dc.description.main: 1 dc.description.tagged: 0 dc.description.totalpages: 117 dc.format.mimetype: application/pdf dc.language.iso: English dc.publisher.digitalrepublisher: Digital Library Of India dc.publisher: I I T Kanpur dc.rights: Out_of_copyright dc.source.library: I I T Kanpur dc.subject.classification: Technology dc.subject.classification: Engineering. Technology In General dc.subject.classification: Electrical Engineering dc.title: Implementation Of Lpc Based Speech Processing Algorithms On Adsp 2100

Book Source: Digital Library of India Item 2015.187878 dc.contributor.author: Peeyush Pandey dc.date.accessioned: 2015-07-07T21:29:34Z dc.date.available: 2015-07-07T21:29:34Z dc.date.digitalpublicationdate: 2005-09-27 dc.identifier.barcode: 1990010089590 dc.identifier.origpath: /rawdataupload/upload/0089/590 dc.identifier.copyno: 1 dc.identifier.uri: http://www.new.dli.ernet.in/handle/2015/187878 dc.description.scanningcentre: IIIT, Allahabad dc.description.main: 1 dc.description.tagged: 0 dc.description.totalpages: 117 dc.format.mimetype: application/pdf dc.language.iso: English dc.publisher.digitalrepublisher: Digital Library Of India dc.publisher: I I T Kanpur dc.rights: Out_of_copyright dc.source.library: I I T Kanpur dc.subject.classification: Technology dc.subject.classification: Engineering. Technology In General dc.subject.classification: Electrical Engineering dc.title: Implementation Of Lpc Based Speech Processing Algorithms On Adsp 2100

Book Source: Digital Library of India Item 2015.187878 dc.contributor.author: Peeyush Pandey dc.date.accessioned: 2015-07-07T21:29:34Z dc.date.available: 2015-07-07T21:29:34Z dc.date.digitalpublicationdate: 2005-09-27 dc.identifier.barcode: 1990010089590 dc.identifier.origpath: /rawdataupload/upload/0089/590 dc.identifier.copyno: 1 dc.identifier.uri: http://www.new.dli.ernet.in/handle/2015/187878 dc.description.scanningcentre: IIIT, Allahabad dc.description.main: 1 dc.description.tagged: 0 dc.description.totalpages: 117 dc.format.mimetype: application/pdf dc.language.iso: English dc.publisher.digitalrepublisher: Digital Library Of India dc.publisher: I I T Kanpur dc.rights: Out_of_copyright dc.source.library: I I T Kanpur dc.subject.classification: Technology dc.subject.classification: Engineering. Technology In General dc.subject.classification: Electrical Engineering dc.title: Implementation Of Lpc Based Speech Processing Algorithms On Adsp 2100

Book Source: Digital Library of India Item 2015.187878 dc.contributor.author: Peeyush Pandey dc.date.accessioned: 2015-07-07T21:29:34Z dc.date.available: 2015-07-07T21:29:34Z dc.date.digitalpublicationdate: 2005-09-27 dc.identifier.barcode: 1990010089590 dc.identifier.origpath: /rawdataupload/upload/0089/590 dc.identifier.copyno: 1 dc.identifier.uri: http://www.new.dli.ernet.in/handle/2015/187878 dc.description.scanningcentre: IIIT, Allahabad dc.description.main: 1 dc.description.tagged: 0 dc.description.totalpages: 117 dc.format.mimetype: application/pdf dc.language.iso: English dc.publisher.digitalrepublisher: Digital Library Of India dc.publisher: I I T Kanpur dc.rights: Out_of_copyright dc.source.library: I I T Kanpur dc.subject.classification: Technology dc.subject.classification: Engineering. Technology In General dc.subject.classification: Electrical Engineering dc.title: Implementation Of Lpc Based Speech Processing Algorithms On Adsp 2100

Book Source: Digital Library of India Item 2015.187878 dc.contributor.author: Peeyush Pandey dc.date.accessioned: 2015-07-07T21:29:34Z dc.date.available: 2015-07-07T21:29:34Z dc.date.digitalpublicationdate: 2005-09-27 dc.identifier.barcode: 1990010089590 dc.identifier.origpath: /rawdataupload/upload/0089/590 dc.identifier.copyno: 1 dc.identifier.uri: http://www.new.dli.ernet.in/handle/2015/187878 dc.description.scanningcentre: IIIT, Allahabad dc.description.main: 1 dc.description.tagged: 0 dc.description.totalpages: 117 dc.format.mimetype: application/pdf dc.language.iso: English dc.publisher.digitalrepublisher: Digital Library Of India dc.publisher: I I T Kanpur dc.rights: Out_of_copyright dc.source.library: I I T Kanpur dc.subject.classification: Technology dc.subject.classification: Engineering. Technology In General dc.subject.classification: Electrical Engineering dc.title: Implementation Of Lpc Based Speech Processing Algorithms On Adsp 2100

With the advancement of technology, the need for a virtual assistant is increasing tremendously. The development of virtual assistants is booming on all platforms. Cortana, Siri are some of the best examples for virtual assistants. We focus on improving the efficiency of virtual assistant by reducing the response time for a particular action. The primary development criterion of any virtual assistant is by developing a simple U.I. for assistant in all platforms and core functioning in the backend so that it could perform well in multi plat formed or cross plat formed manner by applying the backend code for all the platforms. We try a different research approach in this paper. That is, we give computation and processing power to edge devices itself. So that it could perform well by doing actions in a short period, think about the normal working of a typical virtual assistant. That is taking command from the user, transfer that command to the backend server, analyze it on the server, transfer back the action or result to the end user and finally get a response if we could do all this thing in a single machine itself, the response time will get reduced to a considerable amount. In this paper, we will develop a new algorithm by keeping a local database for speech recognition and creating various helpful functions to do particular action on the end device. Akhilesh L "Advanced Virtual Assistant Based on Speech Processing Oriented Technology on Edge Concept (S.P.O.T)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33289.pdf Paper Url: https://www.ijtsrd.com/computer-science/realtime-computing/33289/advanced-virtual-assistant-based-on-speech-processing-oriented-technology-on-edge-concept-spot/akhilesh-l

With the advancement of technology, the need for a virtual assistant is increasing tremendously. The development of virtual assistants is booming on all platforms. Cortana, Siri are some of the best examples for virtual assistants. We focus on improving the efficiency of virtual assistant by reducing the response time for a particular action. The primary development criterion of any virtual assistant is by developing a simple U.I. for assistant in all platforms and core functioning in the backend so that it could perform well in multi plat formed or cross plat formed manner by applying the backend code for all the platforms. We try a different research approach in this paper. That is, we give computation and processing power to edge devices itself. So that it could perform well by doing actions in a short period, think about the normal working of a typical virtual assistant. That is taking command from the user, transfer that command to the backend server, analyze it on the server, transfer back the action or result to the end user and finally get a response if we could do all this thing in a single machine itself, the response time will get reduced to a considerable amount. In this paper, we will develop a new algorithm by keeping a local database for speech recognition and creating various helpful functions to do particular action on the end device. Akhilesh L "Advanced Virtual Assistant Based on Speech Processing Oriented Technology on Edge Concept (S.P.O.T)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33289.pdf Paper Url: https://www.ijtsrd.com/computer-science/realtime-computing/33289/advanced-virtual-assistant-based-on-speech-processing-oriented-technology-on-edge-concept-spot/akhilesh-l

With the advancement of technology, the need for a virtual assistant is increasing tremendously. The development of virtual assistants is booming on all platforms. Cortana, Siri are some of the best examples for virtual assistants. We focus on improving the efficiency of virtual assistant by reducing the response time for a particular action. The primary development criterion of any virtual assistant is by developing a simple U.I. for assistant in all platforms and core functioning in the backend so that it could perform well in multi plat formed or cross plat formed manner by applying the backend code for all the platforms. We try a different research approach in this paper. That is, we give computation and processing power to edge devices itself. So that it could perform well by doing actions in a short period, think about the normal working of a typical virtual assistant. That is taking command from the user, transfer that command to the backend server, analyze it on the server, transfer back the action or result to the end user and finally get a response if we could do all this thing in a single machine itself, the response time will get reduced to a considerable amount. In this paper, we will develop a new algorithm by keeping a local database for speech recognition and creating various helpful functions to do particular action on the end device. Akhilesh L "Advanced Virtual Assistant Based on Speech Processing Oriented Technology on Edge Concept (S.P.O.T)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33289.pdf Paper Url: https://www.ijtsrd.com/computer-science/realtime-computing/33289/advanced-virtual-assistant-based-on-speech-processing-oriented-technology-on-edge-concept-spot/akhilesh-l

With the advancement of technology, the need for a virtual assistant is increasing tremendously. The development of virtual assistants is booming on all platforms. Cortana, Siri are some of the best examples for virtual assistants. We focus on improving the efficiency of virtual assistant by reducing the response time for a particular action. The primary development criterion of any virtual assistant is by developing a simple U.I. for assistant in all platforms and core functioning in the backend so that it could perform well in multi plat formed or cross plat formed manner by applying the backend code for all the platforms. We try a different research approach in this paper. That is, we give computation and processing power to edge devices itself. So that it could perform well by doing actions in a short period, think about the normal working of a typical virtual assistant. That is taking command from the user, transfer that command to the backend server, analyze it on the server, transfer back the action or result to the end user and finally get a response if we could do all this thing in a single machine itself, the response time will get reduced to a considerable amount. In this paper, we will develop a new algorithm by keeping a local database for speech recognition and creating various helpful functions to do particular action on the end device. Akhilesh L "Advanced Virtual Assistant Based on Speech Processing Oriented Technology on Edge Concept (S.P.O.T)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33289.pdf Paper Url: https://www.ijtsrd.com/computer-science/realtime-computing/33289/advanced-virtual-assistant-based-on-speech-processing-oriented-technology-on-edge-concept-spot/akhilesh-l

With the advancement of technology, the need for a virtual assistant is increasing tremendously. The development of virtual assistants is booming on all platforms. Cortana, Siri are some of the best examples for virtual assistants. We focus on improving the efficiency of virtual assistant by reducing the response time for a particular action. The primary development criterion of any virtual assistant is by developing a simple U.I. for assistant in all platforms and core functioning in the backend so that it could perform well in multi plat formed or cross plat formed manner by applying the backend code for all the platforms. We try a different research approach in this paper. That is, we give computation and processing power to edge devices itself. So that it could perform well by doing actions in a short period, think about the normal working of a typical virtual assistant. That is taking command from the user, transfer that command to the backend server, analyze it on the server, transfer back the action or result to the end user and finally get a response if we could do all this thing in a single machine itself, the response time will get reduced to a considerable amount. In this paper, we will develop a new algorithm by keeping a local database for speech recognition and creating various helpful functions to do particular action on the end device. Akhilesh L "Advanced Virtual Assistant Based on Speech Processing Oriented Technology on Edge Concept (S.P.O.T)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33289.pdf Paper Url: https://www.ijtsrd.com/computer-science/realtime-computing/33289/advanced-virtual-assistant-based-on-speech-processing-oriented-technology-on-edge-concept-spot/akhilesh-l

With the advancement of technology, the need for a virtual assistant is increasing tremendously. The development of virtual assistants is booming on all platforms. Cortana, Siri are some of the best examples for virtual assistants. We focus on improving the efficiency of virtual assistant by reducing the response time for a particular action. The primary development criterion of any virtual assistant is by developing a simple U.I. for assistant in all platforms and core functioning in the backend so that it could perform well in multi plat formed or cross plat formed manner by applying the backend code for all the platforms. We try a different research approach in this paper. That is, we give computation and processing power to edge devices itself. So that it could perform well by doing actions in a short period, think about the normal working of a typical virtual assistant. That is taking command from the user, transfer that command to the backend server, analyze it on the server, transfer back the action or result to the end user and finally get a response if we could do all this thing in a single machine itself, the response time will get reduced to a considerable amount. In this paper, we will develop a new algorithm by keeping a local database for speech recognition and creating various helpful functions to do particular action on the end device. Akhilesh L "Advanced Virtual Assistant Based on Speech Processing Oriented Technology on Edge Concept (S.P.O.T)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33289.pdf Paper Url: https://www.ijtsrd.com/computer-science/realtime-computing/33289/advanced-virtual-assistant-based-on-speech-processing-oriented-technology-on-edge-concept-spot/akhilesh-l

With the advancement of technology, the need for a virtual assistant is increasing tremendously. The development of virtual assistants is booming on all platforms. Cortana, Siri are some of the best examples for virtual assistants. We focus on improving the efficiency of virtual assistant by reducing the response time for a particular action. The primary development criterion of any virtual assistant is by developing a simple U.I. for assistant in all platforms and core functioning in the backend so that it could perform well in multi plat formed or cross plat formed manner by applying the backend code for all the platforms. We try a different research approach in this paper. That is, we give computation and processing power to edge devices itself. So that it could perform well by doing actions in a short period, think about the normal working of a typical virtual assistant. That is taking command from the user, transfer that command to the backend server, analyze it on the server, transfer back the action or result to the end user and finally get a response if we could do all this thing in a single machine itself, the response time will get reduced to a considerable amount. In this paper, we will develop a new algorithm by keeping a local database for speech recognition and creating various helpful functions to do particular action on the end device. Akhilesh L "Advanced Virtual Assistant Based on Speech Processing Oriented Technology on Edge Concept (S.P.O.T)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33289.pdf Paper Url: https://www.ijtsrd.com/computer-science/realtime-computing/33289/advanced-virtual-assistant-based-on-speech-processing-oriented-technology-on-edge-concept-spot/akhilesh-l

With the advancement of technology, the need for a virtual assistant is increasing tremendously. The development of virtual assistants is booming on all platforms. Cortana, Siri are some of the best examples for virtual assistants. We focus on improving the efficiency of virtual assistant by reducing the response time for a particular action. The primary development criterion of any virtual assistant is by developing a simple U.I. for assistant in all platforms and core functioning in the backend so that it could perform well in multi plat formed or cross plat formed manner by applying the backend code for all the platforms. We try a different research approach in this paper. That is, we give computation and processing power to edge devices itself. So that it could perform well by doing actions in a short period, think about the normal working of a typical virtual assistant. That is taking command from the user, transfer that command to the backend server, analyze it on the server, transfer back the action or result to the end user and finally get a response if we could do all this thing in a single machine itself, the response time will get reduced to a considerable amount. In this paper, we will develop a new algorithm by keeping a local database for speech recognition and creating various helpful functions to do particular action on the end device. Akhilesh L "Advanced Virtual Assistant Based on Speech Processing Oriented Technology on Edge Concept (S.P.O.T)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33289.pdf Paper Url: https://www.ijtsrd.com/computer-science/realtime-computing/33289/advanced-virtual-assistant-based-on-speech-processing-oriented-technology-on-edge-concept-spot/akhilesh-l

The report treats three topics related to the phenomenon of individual differences in speech: (1) the effect upon speech intelligibility of the listener's familiarity with the speaker's voice; (2) the nature and number of elementary ways, or PAT's (perceived acoustic traits) in which voices are perceived to differ from each other; (3) the physical bases of perceived acoustic traits. Speech intelligibility as measured by the Diagnostic Rhyme Test was unaffected by the degree of the listener's familiarity with the speaker's voice, where the basis of faimiliarity was a maximum of three presentations of a two-minute sample of prose. A voice rating experiment, involving both mono-polar and bi-polar ratings scales failed to reveal any previously unidentified PAT's. A factor analysis of 22 physical and perceptual voice variables yielded results to indicate that the physical correlates of some PAT's may be qualitatively shifted under certain conditions of stimulus impoverishment. The physical bases of the PAT's, Pitch-Magnitude and Animation- Rate appear to be relatively stable under various conditions of stimulus impoverishment, but the physical bases of Loudness-Roughness and Clarity-Beauty may be drastically altered by frequency distortion and vocoderization.

The report contains a discussion of artificial intelligence, computer processing of natural-language data programming languages for problem-solving systems, and structural (contextual, linguistic) pattern recognition. It includes an in-depth survey of recent research results that includes examples of deduction, text recognition, and dialogue by and with computers followed by discussion of speech data, applications of understand-software, and suggestions for further research.

Robust speech processing in practical operating environments requires effective environmental and processor noise suppression. This report describes the technical findings and accomplishments during the reporting period for the research program funded to develop real-time, compressed speech analysis- synthesis algorithms whose performance is invariant under signal contamination. Fulfillment of this requirement is necessary to insure reliable secure compressed speech transmission within realistic military command and control environments. Overall contributions resulting from this research program include the understanding of how environmental noise degrades narrow band, coded speech, development of appropriate real-time noise suppression algorithms, and development of speech parameter identification methods that consider signal contamination as a fundmental element in the estimation process. This report describes the research and results in the areas of noise suppression using the dual input adaptive noise cancellation articulation rate change techniques, spectral subtraction and a description of an experiment which demonstrated that the spectral substraction noise suppression algorithm can improve the intelligibility of 2400 bps, LPC-10 coded, helicopter speech by 10.6 points. In addition summaries are included of prior studies in Constant-Q signal analysis and synthesis, perceptual modelling, speech activity detection, and pole-zero modelling of noisy signals. Three recent studies in speech modelling using the critical band analysis-synthesis transform and using splines are then presented. Finally a list a major publications generated under this contract is given.

In recent years there have been increasing interest in eigenstructure based array signal processing techniques especially in the areas of radar and sonar processing and in signal separation applications as found in communications or speech acquisition/enhancement. However, model errors are not accounted for in these methods. Unfortunately, the problem of model error is a ubiquitous part of real systems: they arise from such phenomena as non-ideal sensor characteristics (eg., sensor calibration errors in phase and gain, etc.), unknown source characteristics and locations, etc. This report is broadly divided into two parts. The first part examines the effects of model errors on the performance of existing eigenstructure based methods in array signal processing. A statistical analysis of the ESPRIT (Estimation of Signal Param via Invariance Techniques) algorithm under random model errors is performed. The analysis provides interesting insight into the sensitivity of ESPRIT to model errors: in particular, for uniform linear arrays of sensors, it is found that the mean square error of DOA (direction of arrival) estimates found using ESPRIT is almost totally dependent on errors in sensor phases and not that of sensor gains.

In recent years there have been increasing interest in eigenstructure based array signal processing techniques especially in the areas of radar and sonar processing and in signal separation applications as found in communications or speech acquisition/enhancement. However, model errors are not accounted for in these methods. Unfortunately, the problem of model error is a ubiquitous part of real systems: they arise from such phenomena as non-ideal sensor characteristics (eg., sensor calibration errors in phase and gain, etc.), unknown source characteristics and locations, etc. This report is broadly divided into two parts. The first part examines the effects of model errors on the performance of existing eigenstructure based methods in array signal processing. A statistical analysis of the ESPRIT (Estimation of Signal Param via Invariance Techniques) algorithm under random model errors is performed. The analysis provides interesting insight into the sensitivity of ESPRIT to model errors: in particular, for uniform linear arrays of sensors, it is found that the mean square error of DOA (direction of arrival) estimates found using ESPRIT is almost totally dependent on errors in sensor phases and not that of sensor gains.

In recent years there have been increasing interest in eigenstructure based array signal processing techniques especially in the areas of radar and sonar processing and in signal separation applications as found in communications or speech acquisition/enhancement. However, model errors are not accounted for in these methods. Unfortunately, the problem of model error is a ubiquitous part of real systems: they arise from such phenomena as non-ideal sensor characteristics (eg., sensor calibration errors in phase and gain, etc.), unknown source characteristics and locations, etc. This report is broadly divided into two parts. The first part examines the effects of model errors on the performance of existing eigenstructure based methods in array signal processing. A statistical analysis of the ESPRIT (Estimation of Signal Param via Invariance Techniques) algorithm under random model errors is performed. The analysis provides interesting insight into the sensitivity of ESPRIT to model errors: in particular, for uniform linear arrays of sensors, it is found that the mean square error of DOA (direction of arrival) estimates found using ESPRIT is almost totally dependent on errors in sensor phases and not that of sensor gains.

In recent years there have been increasing interest in eigenstructure based array signal processing techniques especially in the areas of radar and sonar processing and in signal separation applications as found in communications or speech acquisition/enhancement. However, model errors are not accounted for in these methods. Unfortunately, the problem of model error is a ubiquitous part of real systems: they arise from such phenomena as non-ideal sensor characteristics (eg., sensor calibration errors in phase and gain, etc.), unknown source characteristics and locations, etc. This report is broadly divided into two parts. The first part examines the effects of model errors on the performance of existing eigenstructure based methods in array signal processing. A statistical analysis of the ESPRIT (Estimation of Signal Param via Invariance Techniques) algorithm under random model errors is performed. The analysis provides interesting insight into the sensitivity of ESPRIT to model errors: in particular, for uniform linear arrays of sensors, it is found that the mean square error of DOA (direction of arrival) estimates found using ESPRIT is almost totally dependent on errors in sensor phases and not that of sensor gains.

In recent years there have been increasing interest in eigenstructure based array signal processing techniques especially in the areas of radar and sonar processing and in signal separation applications as found in communications or speech acquisition/enhancement. However, model errors are not accounted for in these methods. Unfortunately, the problem of model error is a ubiquitous part of real systems: they arise from such phenomena as non-ideal sensor characteristics (eg., sensor calibration errors in phase and gain, etc.), unknown source characteristics and locations, etc. This report is broadly divided into two parts. The first part examines the effects of model errors on the performance of existing eigenstructure based methods in array signal processing. A statistical analysis of the ESPRIT (Estimation of Signal Param via Invariance Techniques) algorithm under random model errors is performed. The analysis provides interesting insight into the sensitivity of ESPRIT to model errors: in particular, for uniform linear arrays of sensors, it is found that the mean square error of DOA (direction of arrival) estimates found using ESPRIT is almost totally dependent on errors in sensor phases and not that of sensor gains.

In recent years there have been increasing interest in eigenstructure based array signal processing techniques especially in the areas of radar and sonar processing and in signal separation applications as found in communications or speech acquisition/enhancement. However, model errors are not accounted for in these methods. Unfortunately, the problem of model error is a ubiquitous part of real systems: they arise from such phenomena as non-ideal sensor characteristics (eg., sensor calibration errors in phase and gain, etc.), unknown source characteristics and locations, etc. This report is broadly divided into two parts. The first part examines the effects of model errors on the performance of existing eigenstructure based methods in array signal processing. A statistical analysis of the ESPRIT (Estimation of Signal Param via Invariance Techniques) algorithm under random model errors is performed. The analysis provides interesting insight into the sensitivity of ESPRIT to model errors: in particular, for uniform linear arrays of sensors, it is found that the mean square error of DOA (direction of arrival) estimates found using ESPRIT is almost totally dependent on errors in sensor phases and not that of sensor gains.

In recent years there have been increasing interest in eigenstructure based array signal processing techniques especially in the areas of radar and sonar processing and in signal separation applications as found in communications or speech acquisition/enhancement. However, model errors are not accounted for in these methods. Unfortunately, the problem of model error is a ubiquitous part of real systems: they arise from such phenomena as non-ideal sensor characteristics (eg., sensor calibration errors in phase and gain, etc.), unknown source characteristics and locations, etc. This report is broadly divided into two parts. The first part examines the effects of model errors on the performance of existing eigenstructure based methods in array signal processing. A statistical analysis of the ESPRIT (Estimation of Signal Param via Invariance Techniques) algorithm under random model errors is performed. The analysis provides interesting insight into the sensitivity of ESPRIT to model errors: in particular, for uniform linear arrays of sensors, it is found that the mean square error of DOA (direction of arrival) estimates found using ESPRIT is almost totally dependent on errors in sensor phases and not that of sensor gains.

Speech Processing (eq, multiband compression and dynamics processor) and Field Recording

Speech Processing (eq, multiband compression and dynamics processor) and Field Recording

Speech Processing (eq, multiband compression and dynamics processor) and Field Recording

Speech Processing (eq, multiband compression and dynamics processor) and Field Recording

Speech Processing (eq, multiband compression and dynamics processor) and Field Recording

Speech Processing (eq, multiband compression and dynamics processor) and Field Recording

Speech Processing (eq, multiband compression and dynamics processor) and Field Recording

With the growing demand for speech processing systems in many different languages, there is still a significant bottleneck in building recognition and synthesis support for new languages. The SPICE project is aimed at providing web-based easy-to-use tools for the non-expert to build acoustic and language models for speech recognition and synthesis systems in new languages. This work has required new research in better selection of prompting data, lexicon construction and multilingual acoustic modeling. Where possible synthesis and recognition models are shared. This talk gives an overview of the system, and highlights the specific research issues that have been addressed and what still needs to be done. The system has already been used successfully for some 25 languages. (joint work with Tanja Schultz) ©2008 Microsoft Corporation. All rights reserved.

The rationale behind implementing new information technologies is often to gain productivity improvements associated with the substitution of machinery for labor. However, the literature shows little direct evidence of a positive relationship between information technology investment and subsequent productivity benefits. This thesis reports on the examination into the productivity implications of implementing speech recognition software in a text processing environment. More specifically, research was conducted to compare text processing speeds and error rates using speech recognition software versus the keyboard and mouse. Of interest was the time required to input and proofread text processing tasks as well as the number of errors generated using both methods of text input. The empirical data offer somewhat mixed results. While users initially entered text faster using speech recognition software (p .05), they generated more errors and consequently performed proofreading and error corrections slower using speech. These results suggest that, in terms of accurate text processing, speech recognition software is still not a practical alternative to the keyboard. Therefore, implementation of speech recognition software is unlikely to result in any gains in productivity that would serve to justify its cost.

The rationale behind implementing new information technologies is often to gain productivity improvements associated with the substitution of machinery for labor. However, the literature shows little direct evidence of a positive relationship between information technology investment and subsequent productivity benefits. This thesis reports on the examination into the productivity implications of implementing speech recognition software in a text processing environment. More specifically, research was conducted to compare text processing speeds and error rates using speech recognition software versus the keyboard and mouse. Of interest was the time required to input and proofread text processing tasks as well as the number of errors generated using both methods of text input. The empirical data offer somewhat mixed results. While users initially entered text faster using speech recognition software (p .05), they generated more errors and consequently performed proofreading and error corrections slower using speech. These results suggest that, in terms of accurate text processing, speech recognition software is still not a practical alternative to the keyboard. Therefore, implementation of speech recognition software is unlikely to result in any gains in productivity that would serve to justify its cost.

Natural Language Processing (NLP) is pivotal in the fight against misinformation, offering tools to process and analyze both text and speech. This paper presents the development of NLP algorithms specifically designed for fact-checking, leveraging the capabilities of Question-Answering (QA) systems and knowledge graphs. We conduct a comprehensive analysis of existing QA systems, propose enhancements for fact-checking, and validate our approach through comparative studies. Our findings contribute to the ongoing efforts to improve the reliability of information in the digital age.

Natural Language Processing (NLP) is pivotal in the fight against misinformation, offering tools to process and analyze both text and speech. This paper presents the development of NLP algorithms specifically designed for fact-checking, leveraging the capabilities of Question-Answering (QA) systems and knowledge graphs. We conduct a comprehensive analysis of existing QA systems, propose enhancements for fact-checking, and validate our approach through comparative studies. Our findings contribute to the ongoing efforts to improve the reliability of information in the digital age.

Natural Language Processing (NLP) is pivotal in the fight against misinformation, offering tools to process and analyze both text and speech. This paper presents the development of NLP algorithms specifically designed for fact-checking, leveraging the capabilities of Question-Answering (QA) systems and knowledge graphs. We conduct a comprehensive analysis of existing QA systems, propose enhancements for fact-checking, and validate our approach through comparative studies. Our findings contribute to the ongoing efforts to improve the reliability of information in the digital age.

Natural Language Processing (NLP) is pivotal in the fight against misinformation, offering tools to process and analyze both text and speech. This paper presents the development of NLP algorithms specifically designed for fact-checking, leveraging the capabilities of Question-Answering (QA) systems and knowledge graphs. We conduct a comprehensive analysis of existing QA systems, propose enhancements for fact-checking, and validate our approach through comparative studies. Our findings contribute to the ongoing efforts to improve the reliability of information in the digital age.

Natural Language Processing (NLP) is pivotal in the fight against misinformation, offering tools to process and analyze both text and speech. This paper presents the development of NLP algorithms specifically designed for fact-checking, leveraging the capabilities of Question-Answering (QA) systems and knowledge graphs. We conduct a comprehensive analysis of existing QA systems, propose enhancements for fact-checking, and validate our approach through comparative studies. Our findings contribute to the ongoing efforts to improve the reliability of information in the digital age.

Natural Language Processing (NLP) is pivotal in the fight against misinformation, offering tools to process and analyze both text and speech. This paper presents the development of NLP algorithms specifically designed for fact-checking, leveraging the capabilities of Question-Answering (QA) systems and knowledge graphs. We conduct a comprehensive analysis of existing QA systems, propose enhancements for fact-checking, and validate our approach through comparative studies. Our findings contribute to the ongoing efforts to improve the reliability of information in the digital age.