Dynamic speech models: theory, algorithms, and applications
Dynamic Speech Models provides a comprehensive overview of mathematical models of speech dynamics and addresses the following issues:" How do we make sense of the complex speech process in terms of its functional role of speech communication? " How do we quantify the special role of speech timing?" How do the dynamics relate to the variability of speech which has often been said to seriously hamper automatic speech recognition? " How do we put the dynamic process of speech into a quantitative form to enable detailed analyses? " How can we incorporate the knowledge of speech dynamics into computerized speech analysis and recognition algorithms? The answers to all these questions require building and applying computational models for the dynamic speech process. Such scientific studies help understand why humans speak as they do and how humans exploit redundancy and variability by way of multi-tiered dynamic processes to enhance the efficiency and effectiveness of speech. Second, advancement of human language technology, especially in automatic recognition of human speech is expected to benefit from comprehensive computational modeling of speech dynamics. The limitations of current speech recognition technology are serious and are well known. A commonly acknowledged and frequently discussed weakness of the statistical model underlying current speech recognition technology is the lack of adequate dynamic modeling schemes to provide correlation structure across the temporal speech observation sequence. Dynamic speech modeling may serve as an ultimate solution to this problem.
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A General Modeling and Computational Framework
From Acoustic Dynamics to Hidden Dynamics
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acoustic dynamic models acoustic feature approximation articulatory dynamic model articulatory phonology articulatory target auditory bandwidth basic model cepstral cepstrum chapter coarticulation component computational framework constraints correlation covariance matrix DBN representation Deng denote dependency described discretized hidden dynamic dynamic Bayesian network dynamic speech modeling E-step factorial HMM filter first-order formant forward-backward algorithm frame Gaussian hidden dynamic models hidden dynamic variables hidden Markov model hidden trajectory model IEEE input iterations iy aa iy linear cepstra Markov chain mean vector model of speech model parameters multitiered nonlinear function observation equation optimization output parameter estimation parameter learning phonetic reduction phonological model phonological units piecewise linear quantization random recursion reestimation formula represented residual segmental HMM sequence simplified speaking rate Spectrogram speech acoustics speech chain speech dynamics speech process speech recognition structure target vector Taylor series temporal time-varying TIMIT values vocal tract resonances VTR frequency VTR target VTR tracking