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- W2907124443 abstract "This chapter discusses that Hidden Markov models (HMMs) have become the predominant approach for speech recognition systems. It makes certain structural assumptions, and then tries to learn two sets of parameters from training data. The forward-backward learning algorithm adjusts these model parameters so as to improve the probability that the models generated the training data. This seemingly simple technique has worked surprisingly well, and has led to many state-of-the-art systems. One example of an HMM-based system is SPHINX, a large-vocabulary, speaker-independent, continuous-speech recognition system developed at CMU. The chapter introduces hidden Markov modeling techniques, analyze the reason for their success, and describe some improvements to the standard HMM used is SPHINX. It is believed that these have benefited greatly from the use of detailed subword models, large training databases, and powerful learning techniques." @default.
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- W2907124443 date "1996-01-01" @default.
- W2907124443 modified "2023-09-23" @default.
- W2907124443 title "Speech Recognition Using Hidden Markov Models" @default.
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- W2907124443 doi "https://doi.org/10.1016/b978-044481607-8/50067-0" @default.
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