Investigating lightly supervised acoustic model training

Abstract

The last decade has witnessed substantial progress in speech recognition technology, with todays state-of-the-art systems being able to transcribe broadcast audio data with a word error of about 20%. However, acoustic model development for the recognizers requires large corpora of manually transcribed training data. Obtaining such data is both time… (More)
DOI: 10.1109/ICASSP.2001.940871

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