Acoustic Confidence Measures for Segmenting Broadcast News

In this paper we define an acoustic confidence measure based on the estimates of local posterior probabilities produced by a HMM/ANN large vocabulary continuous speech recognition system. We use this measure to segment continuous audio into regions where it is and is not appropriate to expend recognition effort. The segmentation is computationally inexpensive and provides reductions in both overall word error rate and decoding time. The technique is evaluated using material from the Broadcast News corpus

Acoustic Confidence Measures For Segmenting Broadcast News

In this paper we define an acoustic confidence measure based on the estimates of local posterior probabilities produced by a HMM/ANN large vocabulary continuous speech recognition system. We use this measure to segment continuous audio into regions where it is and is not appropriate to expend recognition effort. The segmentation is computationally inexpensive and provides reductions in both overall word error rate and decoding time. The technique is evaluated using material from the Broadcast News corpus. 1. INTRODUCTION Most speech recognition tasks to date have required the recognition of discrete utterances over which both the speaker and channel characteristics remain constant. It is given that the data supplied to the recogniser is speech and so speech detection amounts to little more than trimming off leading and trailing silences. However, practical speech recognition systems cannot expect to be supplied with such pre-segmented data. Faced with an unsegmented stream of audio, f..