We propose an acoustic model training method which combines committee-based active learning and semi-supervised learning for large
vocabulary continuous speech recognition. In this method, each untranscribed training utterance is examined by a committee consists
of multiple speech recognizers and the degree of disagreement in the committee on its transcription is used for selecting utterances.
Those utterances the committee members disagree with each other are transcribed for active learning, while those they agree are used
for semi-supervised learning. Our method was evaluated using the Corpus of Spontaneous Japanese. It was shown that it achieved higher
recognition accuracy with lower transcription costs than random sampling, active learning alone, and semi-supervised learning alone. We
also propose an alternative data selection method in semi-supervised learning.
各種検索
サポート
ヘルプ