Multi-keyword spotting of telephone speech using a fuzzy search algorithm and keyword-driven two-level CBSM

In telephone speech recognition, the acoustic mismatch between training and testing environments often causes a severe degradation in the recognition performance. This paper presents a keyword-driven two-level codebook-based stochastic matching (CBSM) algorithm to eliminate the acoustic mismatch. Additionally, in Mandarin speech, it is dicult to correctly recognize the unvoiced part in a syllable. In order to reduce the recognition error of unvoiced segments, a fuzzy search algorithm is proposed to extract keyword candidates from a syllable lattice. Finally, a keyword relation and a weighting function for keyword combinations are presented for multi-keyword spotting. In the multikeyword spotting of Mandarin speech, 94 right context-dependent and 38 context-independent subsyllables are used as the basic recognition units. A corresponding anti-subsyllable model for each subsyllable is trained and used for veri®cation. In this system, 2583 faculty names and 39 department names are selected as the primary keywords and the secondary keywords, respectively. Using a testing set of 3088 conversational speech utterances from 33 speakers (20 male, 13 female), these techniques reduced the recognition error rate from 29.6% to 20.6% for multi-keywords embedded in non-keyword speech. Ó 2001 Elsevier Science B.V. All rights reserved.