Techniques to Achieve an Accurate Real-Time Large-Vocabulary Speech Recognition System.

摘要

In addressing the problem of achieving high-accuracy real-time speech recognition systems, we focus on recognizing speech from ARPA's 20,000- word Wall Street Journal (WSJ) task, using current UNIX workstations. We have found that our standard approach-using a narrow beam width in a viterbi search for simple discrete-density hidden Markov models (HMMs)-works in real time with only very low accuracy. Our most accurate algorithms recognize speech many times slower than real time. Our (yet unattained) goal is to recognize speech in real time at or near full accuracy.