Unsupervised Vocabulary Expansion for Automatic Transcription of Broadcast News
Katsutoshi Ohtsuki, Nobuaki Hiroshima, M. Oku, A. Imamura · 2006
We present an unsupervised vocabulary adaptation method for large vocabulary continuous speech recognition based on relevant word extraction. This method addresses the out-of-vocabulary (OOV) problem, which is one of the most challenging problems in current automatic speech recognition (ASR) systems. Words relevant to the content of the input speech are extracted from a vocabulary database, based on speech recognition results obtained in the first recognition process using a reference vocabulary. The relevance between words is calculated based on concept vectors, which are trained using word cooccurrence statistics. An expanded vocabulary that includes fewer OOV words is built by adding the extracted words to the reference vocabulary and used for the second recognition process. The experimental results for broadcast news speech show that our method achieves a 30% reduction in the OOV rate and also improves speech recognition accuracy.