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Contents
Common terms and phrasesadaptation sentences April Bahl baseform beam search Bigram bilinear transform BYBLOS cepstrum cepstrum coefficients codebooks computed Conference on Acoustics context context-dependent continuous speech recognition Correct cruisers database deleted interpolation dictionary discrete HMMs distribution duration DX IX DD DX IY error rate estimates EY DX forward algorithm forward-backward algorithm frigates function words function-word-dependent phone modeling Hidden Markov Models IEEE International Conference improvements input insertion/deletion modeling interpolated re-estimation Isolated Word isolated-word iteration Jelinek language model large vocabulary null transition output pdf perplexity phoneme problem pronunciation Rabiner recognizer Schwartz sequence Shikano ships Signal Processing Speaker Adaptation speaker-independent Speech Recognition System SPHINX System Table task trainable training data training speakers transition probabilities triphone models units of speech vector quantization Viterbi algorithm Viterbi search word accuracy word models Word Recognition word-dependent phone modeling Word-pair Popular passagesPage xv - ... 635 Mbytes per hour for uncompressed speech, without any noticeable loss of quality. Speech Recognition. Speech recognition has a long history of being one of the difficult problems in artificial intelligence (AI) and computer science. As one goes from problem solving tasks in AI to perceptual tasks, the problem characteristics change dramatically: knowledge poor to knowledge rich; low data rates to high data rates; slow response time (minutes to hours) to instantaneous response time. These characteristics... Page 98 - ... phone-in-context. A context usually refers to the immediate left and/or right neighboring phones. A left-context dependent phone is dependent on the left context, while a right-context dependent phone is dependent on the right context. A triphone model takes into consideration both the left and the right neighboring phones; if two phones have the same identity but different left or right context, they are considered different triphones. Triphone models are usually poorly trained because there... Page 191 - The acoustic-modeling problem in automatic speech recognition. Ph.D. Thesis, Computer Science Department, Carnegie Mellon University. Page 13 - HMMs have two stochastic processes which enable the modeling not only of acoustic phenomena, but also of timescale distortions. Furthermore, efficient algorithms exist for accurate estimation of HMM parameters. Unlike other non-parametric and ad-hoc approaches, the forward-backward re-estimation algorithm for hidden Markov models is an instance of the EM algorithm [Baum 72]. As such, every iteration of the algorithm results in an improved set of model parameters. Hidden Markov models are a succinct... Page 20 - A is a set of transitions {</,,} where a,; is the probability of taking a transition from state i to state ;', and , atj = 1. References to this bookFrom other books
From Google ScholarTree-Based State Tying for High Accuracy ModellingSJ Young, JJ Odell, PC Woodland Understanding Spontaneous SpeechWayne Ward Rapid Speaker Adaptation in Eigenvoice SpaceRoland Kuhn, Jean-Claude Junqua, Patrick Nguyen, Nancy Niedzielski - 2000 - IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING References from web pagesRECENT PROGRESS IN THE SPHINX SPEECH RECOGNITION SYSTEM Speech Recognition: Past, Present, and Future Acoustic modeling of subword units for large vocabulary speaker ... The Development of Automatic Speech Recognition Software for ... Automatic cueing of speech - US Patent 6317716 From rmyers@ics.uci.edu Wed Jun 1 01:20:00 1994 Received: from ... Corporate Counsel Center - Methods and apparatus for handwriting recognition - Patent 6556712 @book{Row94, author = {Rowe, gw}, title = {Theoretical models in ... Voice activated communication system and program guide - Patent ... Bibliographic information
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