AUTOMATIC RECOGNITION OF KEYWORDS IN UNCONSTRAINED SPEECH USING HIDDEN MARKOV-MODELS

作     者：WILPON, JG RABINER, LR LEE, CH GOLDMAN, ER 

作者机构：AT and T Bell Laboratories Inc. Murray Hill NJ USA AT&T Bell Lab. Murray Hill NJ USA 

出 版 物：《IEEE TRANSACTIONS ON ACOUSTICS SPEECH AND SIGNAL PROCESSING》 (IEEE Trans. Acoust. Speech Sign. Proces.)

年 卷 期：1990年第38卷第11期

页      面：1870-1878页

核心收录：

主　　题：Automatic speech recognition Hidden Markov models Vocabulary Speech recognition Telephony Isolation technology Large-scale systems Algorithm design and analysis Speech enhancement Intelligent networks 

摘      要：The modifications made to a connected word speech recognition algorithm based on hidden Markov models (HMMs) which allow it to recognize words from a predefined vocabulary list spoken in an unconstrained fashion are described. The novelty of this approach is that statistical models of both the actual vocabulary word and the extraneous speech and background are created. An HMM-based connected word recognition system is then used to find the best sequence of background, extraneous speech, and vocabulary word models for matching the actual input. Word recognition accuracy of 99.3% on purely isolated speech (i.e., only vocabulary items and background noise were present), and 95.1% when the vocabulary word was embedded in unconstrained extraneous speech, were obtained for the five word vocabulary using the proposed recognition algorithm.