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Digital speech processing, synthesis, and recognition
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This paper presents principal characteristics of speech speech production models speech analysis and analysis-synthesis systems linear predictive coding (LPC) analysis speech coding speech synthesis speech recognition future directions of speech processing.Abstract:
Principal characteristics of speech speech production models speech analysis and analysis-synthesis systems linear predictive coding (LPC) analysis speech coding speech synthesis speech recognition future directions of speech processing. Appendices: convolution and z-transform vector quantization algorithm neural nests.read more
Citations
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Journal ArticleDOI
Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups
Geoffrey E. Hinton,Li Deng,Dong Yu,George E. Dahl,Abdelrahman Mohamed,Navdeep Jaitly,Andrew W. Senior,Vincent Vanhoucke,Patrick Nguyen,Tara N. Sainath,Brian Kingsbury +10 more
TL;DR: This article provides an overview of progress and represents the shared views of four research groups that have had recent successes in using DNNs for acoustic modeling in speech recognition.
Journal Article
Deep Neural Networks for Acoustic Modeling in Speech Recognition
Geoffrey E. Hinton,Li Deng,Dong Yu,George E. Dahl,Abdelrahman Mohamed,Navdeep Jaitly,Andrew W. Senior,Vincent Vanhoucke,Patrick Nguyen,Tara N. Sainath,Brian Kingsbury +10 more
TL;DR: This paper provides an overview of this progress and repres nts the shared views of four research groups who have had recent successes in using deep neural networks for a coustic modeling in speech recognition.
Journal ArticleDOI
Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images.
TL;DR: In this study the optic disc, blood vessels, and fovea were accurately detected and the identification of the normal components of the retinal image will aid the future detection of diseases in these regions.
Journal ArticleDOI
Speech emotion recognition using hidden Markov models
TL;DR: This paper proposes a text independent method of emotion classification of speech that makes use of short time log frequency power coefficients (LFPC) to represent the speech signals and a discrete hidden Markov model (HMM) as the classifier.
Book
Survey of the State of the Art in Human Language Technology
TL;DR: In this article, the authors present a glossary for language analysis and understanding in the context of spoken language input and output technologies, and evaluate their work with a set of annotated corpora.
References
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Journal ArticleDOI
Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups
Geoffrey E. Hinton,Li Deng,Dong Yu,George E. Dahl,Abdelrahman Mohamed,Navdeep Jaitly,Andrew W. Senior,Vincent Vanhoucke,Patrick Nguyen,Tara N. Sainath,Brian Kingsbury +10 more
TL;DR: This article provides an overview of progress and represents the shared views of four research groups that have had recent successes in using DNNs for acoustic modeling in speech recognition.
Journal Article
Deep Neural Networks for Acoustic Modeling in Speech Recognition
Geoffrey E. Hinton,Li Deng,Dong Yu,George E. Dahl,Abdelrahman Mohamed,Navdeep Jaitly,Andrew W. Senior,Vincent Vanhoucke,Patrick Nguyen,Tara N. Sainath,Brian Kingsbury +10 more
TL;DR: This paper provides an overview of this progress and repres nts the shared views of four research groups who have had recent successes in using deep neural networks for a coustic modeling in speech recognition.
Journal ArticleDOI
Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images.
TL;DR: In this study the optic disc, blood vessels, and fovea were accurately detected and the identification of the normal components of the retinal image will aid the future detection of diseases in these regions.
Journal ArticleDOI
Speech emotion recognition using hidden Markov models
TL;DR: This paper proposes a text independent method of emotion classification of speech that makes use of short time log frequency power coefficients (LFPC) to represent the speech signals and a discrete hidden Markov model (HMM) as the classifier.
Book
Survey of the State of the Art in Human Language Technology
TL;DR: In this article, the authors present a glossary for language analysis and understanding in the context of spoken language input and output technologies, and evaluate their work with a set of annotated corpora.