tailieunhanh - Dynamic Speech ModelsTheory, Algorithms, and Applications phần 4

Tham khảo tài liệu 'dynamic speech modelstheory, algorithms, and applications phần 4', kỹ thuật - công nghệ, kĩ thuật viễn thông phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 26 DYNAMIC SPEECH MODELS where we assume that any inaccuracy in the parametric model of Eq. can be represented by residual random noise e k . This noise is assumed to be IID and zero-mean Gaussian N e k 0 Se . This then specifies the conditional PDF of Eq. to be Gaussian of N y k m Se where the mean vector m is the right-hand side of Eq. . It is well known that the behavior of articulation and subsequent acoustics is subject to modification under severe environmental distortions. This modification sometimes called Lombard effect can take a number of possible forms including articulatory target overshoot articulatory target shift hyper-articulation or increased articulatory efforts by modifying the temporal course of the articulatory dynamics. The Lombard effect has been very difficult to represent in the conventional HMM framework since there is no articulatory representation or any similar dynamic property therein. Given the generative model of speech described here that explicitly contains articulatory variables the Lombard effect can be naturally incorporated. Fig. shows the DBN that incorporates Lombard effect in the comprehensive generative model of speech. It is represented by the feedback dependency from the noise and h-distortion nodes to the articulator nodes in the DBN. The nature of the feedback maybe represented in the form of hyper-articulation where the time constant in the articulatory dynamic equation is reduced to allow for more rapid attainment of the given articulatory target which is sampled from the target distribution . The feedback for Lombard effect may alternatively take the form of target overshoot where the articulatory dynamics exhibit oscillation around the articulatory target. Finally the feedback may take the form of target elevation where the mean vector of the target distribution is shifted further away from the target value of the preceding phonological state compared with the situation when no Lombard effect .

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