Utterance verification of keyword strings using word-based minimum verification error (WB-MVE) training

Authors:
R. A. Sukkar;A. R. Setlur;M. G. Rahim;Chin-Hui Lee
Affiliations:
AT&TBell Labs., Naperville, IL, USA;Sch. of Comput. Sci., Carnegie Mellon Univ., Pittsburgh, PA, USA;Interactive Syst. Lab., Carnegie Mellon Univ., Pittsburgh, PA, USA;Interactive Syst. Lab., Carnegie Mellon Univ., Pittsburgh, PA, USA
Venue:
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01
Year:
1996

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Abstract

An utterance verification method based on minimum verification error training is presented. In a two-stage process, the recognition hypothesis produced by an HMM-based speech recognizer is verified using a set of verification-specific models that are independent of the models used in the recognition process. The verification models are trained using a discriminative training procedure that seeks to minimize the verification error by simultaneously maximizing the rejection of non-keywords and misrecognized keywords while minimizing the rejection of correctly recognized keywords. This method is evaluated on a connected digit recognition task with a null grammar. The baseline string error rate for this task was 4.85%. At 5% rejection of valid strings, the string error rate decreased to 2.70% using the proposed verification method. The corresponding performance on non-keyword speech was a rejection rate of over 99.0%.