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Two-pass strategy for continuous speech recognition with detection and transcription of unknown words

This paper proposes a new approach of using a two-pass strategy to effectively recognize continuous speech including unknown words. In this approach, the first pass uses context-independent phoneme HMMs to recognize registered words and phoneme-cluster HMMs to detect unknown words. In the second pass context-dependent phone models are used for precise recognition where unknown words are transcribed. In sentence recognition experiments using this unknown-word processing, phoneme cluster models that consider the Japanese syllabic construction achieved a higher word accuracy rate of 70.3%, compared with 59.2% for sentence recognition without this processing. Furthermore, the amount of processing was reduced by about half compared with a detection method using phoneme HMMs. The total system achieves a 75.2% phoneme accuracy rate including the transcription of unknown words.