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This is a fantastic book because it succinctly lays out many of the fundamentals of the field. I find myself repeatedly turning to it because I know it's one place where I'll find a clear and precise explanation of many basic concepts and algorithms that are standard in the field today.
acoustic model acoustic processor allophones applied arg max basic Baum algorithm Baum-Welch algorithm beam search belonging bigram building blocks chapter classification code word composite HMM compute concatenation conditional entropy Conference on Acoustics constraints context corresponding criterion decision tree decoding defined denotes derived determined elementary HMMs encoding equivalence classes estimate evaluate fast match fenonic base forms formula function hidden Markov models histories h HMM of figure HMM parameters IEEE Transactions Information Theory iterative L.R. Bahl language model large vocabulary leaf Markov chain maximize method mutual information node non-null transitions null transitions observed output sequence output string output symbol path possible proba probability distribution problem produced question R.L. Mercer recognizer recursion relative frequency result segments Signal Processing speaker Speaker Recognition specified speech recognition split stack subset tion training data trellis trigram language model triphone vector quantization Viterbi algorithm word sequence word string
Statistical Methods for Speech Recognition - The MIT Press
Statistical methods for speech recognition
Statistical Methods for Speech Recognition
JSTOR: Statistical Methods for Speech Recognition
Book Reviews: Corpus-Based Methods in Language and Speech Processing
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Hidden Markov Models for Speech Recognition | Spring 2004
System and methods for acoustic and language modeling for ...