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Titlebook: Automatic Speech Recognition; The Development of t Kai-Fu Lee Book 1989 Springer Science+Business Media New York 1989 N-Gramm.Symbol.artifi

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樓主: Nixon
21#
發(fā)表于 2025-3-25 04:08:49 | 只看該作者
Summary of Results,Figure 8-1 shows improvements from earlier versions of SPHINX. The seven versions in Figure 8-1 correspond to the following descriptions with incremental improvements:
22#
發(fā)表于 2025-3-25 09:15:34 | 只看該作者
https://doi.org/10.1007/978-1-4615-3650-5N-Gramm; Symbol; artificial intelligence; behavior; cognition; complexity; grammar; hidden Markov model; int
23#
發(fā)表于 2025-3-25 13:16:54 | 只看該作者
978-1-4613-6624-9Springer Science+Business Media New York 1989
24#
發(fā)表于 2025-3-25 18:51:46 | 只看該作者
25#
發(fā)表于 2025-3-25 22:17:06 | 只看該作者
26#
發(fā)表于 2025-3-26 01:14:18 | 只看該作者
27#
發(fā)表于 2025-3-26 06:54:21 | 只看該作者
28#
發(fā)表于 2025-3-26 12:02:34 | 只看該作者
https://doi.org/10.1007/978-1-84628-915-6hapters, we have used phones as the fundamental unit of speech. An even more natural unit is words. In this chapter, we will discuss the strengths and weaknesses of word and phone models, as well as a number of other units proposed by earlier work. Then, we will propose two new units that will subst
29#
發(fā)表于 2025-3-26 12:55:19 | 只看該作者
30#
發(fā)表于 2025-3-26 19:29:58 | 只看該作者
Changes for Global Products and PLM,ov modeling, a powerful mathematical learning paradigm. We also decided to use vector quantization and discrete HMMs for expedience and practicality. Then we attacked the problems of large vocabulary, speaker independence, and continuous speech within our discrete HMM framework.
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