Automatic recognition of keywords in unconstrained speech using hidden Markov models

Author

Wilpon, Jay G. ; Rabiner, Lawrence R. ; Lee, Chin-Hui ; Goldman, E.R.

Author_Institution

AT&T Bell Lab., Murray Hill, NJ, USA

Volume

38

Issue

11

fYear

1990

fDate

11/1/1990 12:00:00 AM

Firstpage

1870

Lastpage

1878

Abstract

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

Keywords

Markov processes; speech recognition; automatic keyword recognition; background; connected word speech recognition algorithm; hidden Markov models; predefined vocabulary list; unconstrained extraneous speech; vocabulary word models; Algorithm design and analysis; Automatic speech recognition; Hidden Markov models; Intelligent networks; Isolation technology; Large-scale systems; Speech enhancement; Speech recognition; Telephony; Vocabulary;

fLanguage

English

Journal_Title

Acoustics, Speech and Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

0096-3518

Type

jour

DOI

10.1109/29.103088

Filename

103088