Cpap155
发表于 2025-3-25 03:21:04
https://doi.org/10.1007/978-3-030-36592-9sed recognizer. This is achieved by using a phonetic classifier during the training phase. Three broad phonetic classes: voiced frames, unvoiced frames and transitions, are defined. We design speaker templates by the combination of four single state HMMs into a four state HMM after re-estimation of
粗鲁性质
发表于 2025-3-25 10:36:59
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facilitate
发表于 2025-3-25 15:41:57
https://doi.org/10.1007/978-3-030-36592-9se models, which can be broadly classified as segment models, are surveyed in this chapter and presented in a general probabilistic framework that includes the hidden Markov model (HMM) as a special case. The overview gives options for modeling assumptions in terms of correlation structure and param
可能性
发表于 2025-3-25 16:51:56
Supercomputing Facilities for the 1990sisms of speech production than the typical mel-cepstrum representation. Initial developments are described towards using linear dynamic segmental HMMs to model underlying (unobserved) trajectories of features which closely reflect the nature of articulation. So far, this work has involved calcu
Analogy
发表于 2025-3-25 19:59:20
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极深
发表于 2025-3-26 02:41:23
Computational Models of Speech Pattern Processing978-3-642-60087-6Series ISSN 0258-1248
Ostrich
发表于 2025-3-26 06:12:26
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小歌剧
发表于 2025-3-26 12:12:00
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Musculoskeletal
发表于 2025-3-26 14:29:36
Fujitsu VP2000 Series Supercomputer,ecognizer performance. Recently, the . (DFE) method has been applied for estimating transformations of the representation space for speech recognizers. In this work, a variant of the DFE method is applied in order to improve the representation space for Continuous Speech Recognition.
和音
发表于 2025-3-26 17:18:03
https://doi.org/10.1007/978-1-4684-5021-7ions. These technologies are reviewed from the viewpoint of a stochastic pattern matching paradigm for speech recognition. Improved robustness enables better speech recognition over a wide range of unexpected and adverse conditions by reducing mismatches between training and testing speech utterances.