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Dynamic Speech ModelsTheory, Algorithms, and Applications phần 3

Tham khảo tài liệu 'dynamic speech modelstheory, algorithms, and applications phần 3', kỹ thuật - công nghệ, kĩ thuật viễn thông phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 12 DYNAMIC SPEECH MODELS more casual or relaxed the speech style is the greater the overlapping across the feature gesture dimensions becomes. Second phonetic reduction occurs where articulatory targets as phonetic correlates to the phonological units may shift towards a more neutral position due to the use of reduced articulatory efforts. Phonetic reduction also manifests itself by pulling the realized articulatory trajectories further away from reaching their respective targets due to physical inertia constraints in the articulatory movements. This occurs within generally shorter time duration in casual-style speech than in the read-style speech. It seems difficult for the HMM systems to provide effective mechanisms to embrace the huge new acoustic variability in casual spontaneous and conversational speech arising either from phonological organization or from phonetic reduction. Importantly the additional variability due to phonetic reduction is scaled continuously resulting in phonetic confusions in a predictable manner. See Chapter 5 for some detailed computation simulation results pertaining to such prediction. Due to this continuous variability scaling very large amounts of labeled speech data would be needed. Even so they can only partly capture the variability when no structured knowledge about phonetic reduction and about its effects on speech dynamic patterns is incorporated into the speech model underlying spontaneous and conversational speech-recognition systems. The general design philosophy of the mathematical model for the speech dynamics described in this chapter is based on the desire to integrate the structured knowledge of both phonological reorganization and phonetic reduction. To fully describe this model we break up the model into several interrelated components where the output expressed as the probability distribution of one component serves as the input to the next component in a generative spirit. That is we characterize each model .