Evolution of the performance of automatic speech recognition algorithms in transcribing conversational telephone speech

Abstract

Research in the speech recognition speech-to-text conversion area has been underway for a couple of decades, and a great deal of progress has been made in reducing the word error rate (WER). In this paper, we attempt to summarize the state of the art in speech recognition algorithms. The algorithms we describe span the areas of lexicon design, feature extraction, classifier design, combination of hypotheses, and speaker adaptation of acoustic models. We will benchmark the algorithms on two main sources of speech, the first being Voicemail (conversational telephone speech from a single speaker) and the second being Switchboard (conversational telephone speech between two speakers). We also present the results of some cross-domain experiments which highlight the "brittleness" of speech recognition systems today and illustrates the need to focus research effort on improving cross-domain performance.