Here are the comprehensive details on cutting edge technologies employing neural networks for speech recognition and speech processing in modern communications. Going far beyond the simple speech recognition technologies on the market today, this new book, written by and for speech and signal processing engineers in industry, R&D, and academia, takes you to the forefront of the hottest emergent neural net-based speech processing techniques.
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Inside This Book
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First Sentence:
A speech signal is an acoustic waveform; it is also a principal medium for conveying various kinds of information useful for human communications. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
error count loss, formant transformation, speaker recognition applications, empirical average loss, vocal tract system, misclassification measure, average empirical loss, generalized probabilistic descent method, quefrency region, formant contours, formant data, probabilistic descent theorem, acoustic feature vector, classification error count, cohort normalization, discriminative design, discriminative training methods, error probability situation, discriminative feature extraction, voice conversion, neural tree networks, discriminant boundaries, discriminant function classifier, optimal design condition, target speaker Key Phrases - Capitalized Phrases (CAPs): (learn more)
New York, Englewood Cliffs, Int Conf Acoustics, Academic Press, Proc Int, Prentice Hall, San Francisco, Carnegie Mellon University, Morgan Kaufmann, Spoken Language Processing, Discriminative Prototype-Based Methods, Kluwer Academic Publishers, Stochastic Networks, Acoustical Society of Japan, Cambridge University Engineering Department, Current Issues, Indian Institute of Technology, San Mateo, Speecb Recognition, Acottst Soc, Acoust Soc Ant, Bell Labs E-set, Derivative of Observation Probability, Novel Approach, Theory of Adaptive Pattern Classifiers New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
A speech signal is an acoustic waveform; it is also a principal medium for conveying various kinds of information useful for human communications. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
error count loss, formant transformation, speaker recognition applications, empirical average loss, vocal tract system, misclassification measure, average empirical loss, generalized probabilistic descent method, quefrency region, formant contours, formant data, probabilistic descent theorem, acoustic feature vector, classification error count, cohort normalization, discriminative design, discriminative training methods, error probability situation, discriminative feature extraction, voice conversion, neural tree networks, discriminant boundaries, discriminant function classifier, optimal design condition, target speaker Key Phrases - Capitalized Phrases (CAPs): (learn more)
New York, Englewood Cliffs, Int Conf Acoustics, Academic Press, Proc Int, Prentice Hall, San Francisco, Carnegie Mellon University, Morgan Kaufmann, Spoken Language Processing, Discriminative Prototype-Based Methods, Kluwer Academic Publishers, Stochastic Networks, Acoustical Society of Japan, Cambridge University Engineering Department, Current Issues, Indian Institute of Technology, San Mateo, Speecb Recognition, Acottst Soc, Acoust Soc Ant, Bell Labs E-set, Derivative of Observation Probability, Novel Approach, Theory of Adaptive Pattern Classifiers New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 32
books:
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6
books
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- Fundamentals of Speech Recognition by Lawrence Rabiner on page 18, page 343, and Back Matter
- Digital Neural Networks by S.Y. Kung on page 18, page 119, and page 343
- Pattern Classification (2nd Edition) by Richard O. Duda on page 344, and page 390
- Data Compression by Lee D. Davisson on page 467, and page 468
- Speech Enhancement (Prentice-Hall Signal Processing Series) by Jae S. Lim in Back Matter (1), and Back Matter (2)
See all 32 books this book cites
- Pattern Recognition in Speech and Language Processing (Electrical Engineering & Applied Signal Processing Series) by Wu Chou on page 144, and page 145
- Digital Signal Processing and Statistical Classification by George J. Miao in Back Matter
- Handbook of Neural Network Signal Processing (Electrical Engineering & Applied Signal Processing Series) by Yu Hen Hu on page 14
- Statistical and Adaptive Signal Processing: Spectral Estimation, Signal Modeling, Adaptive Filtering and Array Processing (Artech House Signal Processing Library) by Dimitris G. Manolakis in Front Matter
- Signal Processing Fundamentals and Applications for Communications and Sensing Systems (Artech House Signal Processing Library) by John Minkoff in Back Matter
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