By Li Deng
What are the compelling purposes for undertaking dynamic speech modeling? we offer the reply in similar features. First, clinical inquiry into the human speech code has been relentlessly pursued for numerous a long time. As an important provider of human intelligence and data, speech is the main average kind of human verbal exchange. Embedded within the speech code are linguistic (as good as para-linguistic) messages, that are conveyed via 4 degrees of the speech chain. Underlying the powerful encoding and transmission of the linguistic messages are the speech dynamics in any respect the 4 degrees. Mathematical modeling of speech dynamics presents a good device within the medical tools of learning the speech chain. Such medical experiences aid comprehend why people converse as they do and the way people take advantage of redundancy and variability in terms of multitiered dynamic methods to reinforce the potency and effectiveness of human speech verbal exchange. moment, development of human language expertise, particularly that during computerized acceptance of natural-style human speech is usually anticipated to profit from accomplished computational modeling of speech dynamics. the restrictions of present speech acceptance know-how are severe and are popular. A mostly stated and often mentioned weak spot of the statistical version underlying present speech attractiveness know-how is the inability of sufficient dynamic modeling schemes to supply correlation constitution around the temporal speech statement series. regrettably, because of quite a few purposes, the vast majority of present learn actions during this zone desire purely incremental variations and enhancements to the present HMM-based state of the art. for instance, whereas the dynamic and correlation modeling is understood to be a big subject, many of the structures however hire simply an ultra-weak type of speech dynamics; e.g., differential or delta parameters. Strong-form dynamic speech modeling, that is the point of interest of this monograph, may perhaps function an final option to this challenge. After the creation bankruptcy, the most physique of this monograph contains 4 chapters. They disguise numerous points of concept, algorithms, and functions of dynamic speech versions, and supply a entire survey of the study paintings during this zone spanning over previous 20~years. This monograph is meant as complex fabrics of speech and sign processing for graudate-level educating, for pros and engineering practioners, in addition to for pro researchers and engineers really good in speech processing.
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Additional resources for Dynamic Speech Models - Theory, Algorithms and Applications (Synthesis Lectures on Speech and Audio Processing)
Explicitly added is the dependency of the parameters ( (s )) of the articulatory dynamic model on the phonological state In Fig. 11 where o(k) is specified in Eq. cls 26 May 30, 2006 12:56 DYNAMIC SPEECH MODELS where we assume that any inaccuracy in the parametric model of Eq. 11) can be represented by residual random noise [k]. This noise is assumed to be IID and zero-mean Gaussian: N [ (k); 0, ]. This then specifies the conditional PDF of Eq. 12) to be Gaussian of N [y(k); m, ], where the mean vector m is the right-hand side of Eq.
The forward recursive formula is S C α(s t+1 , it+1 ) = α(s t , it ) p(s t+1 , it+1 |s t , it ) p(o t+1 |s t+1 , it+1 ). 12) s t =1 it =1 Proof of Eq. 12): α(s t+1 , it+1 ) ≡ p(o 1t+1 , s t+1 , it+1 ) = p(o 1t , o t+1 , s t+1 , it+1 , s t , it ) st it p(o t+1 , s t+1 , it+1 | o 1t , s t , it ) p(o 1t , s t , it ) = st it st it st it st it p(o t+1 , s t+1 , it+1 | s t , it )α(s t , it ) = p(o t+1 | s t+1 , it+1 , s t , it ) p(s t+1 , it+1 | s t , it )α(s t , it ) = p(o t+1 | s t+1 , it+1 ) p(s t+1 , it+1 | s t , it )α(s t , it ).
Each region, s , of such dynamics is characterized by the s -dependent parameter set Λs , with the “state noise” denoted by ws (k). The memoryless nonlinear mapping function is exploited to link the hidden dynamic vector z(k) to the observed acoustic feature vector o(k), with the “observation noise” denoted by vs (k), and also parameterized by region-dependent parameters. 4) form a general multiregion nonlinear dynamic system model: z(k + 1) = gk [z(k), Λs ] + ws (k), o(k ) = hk [z(k ), Ωs ] + vs (k ).