Improving utterance verification using hierarchical confidence measures in continuous natural numbers recognition

Utterance verification (UV) is a critical function of an automatic speech recognition (ASR) system working on real applications where spontaneous speech, out-of-vocabulary (OOV) words and acoustic noise are present. We present a new UV procedure with two major features. (a) Confidence tests are applied to decoded string hypotheses obtained from using word and garbage models that represent OOV words and noise. Thus the ASR system is designed to deal with what we refer to as word spotting and noise spotting capabilities. (b) The UV procedure is based on three different confidence tests, two based on acoustic measures and one founded on linguistic information, applied in a hierarchical structure. Experimental results from a real telephone application on a natural number recognition task show a 50% reduction in recognition errors with a moderate 12% rejection rate of correct utterances and a low 1.5% rate of false acceptance