Acoustic Model Bootstrapping Using Semi-Supervised Learning

This work aims at bootstrapping acoustic model training for automatic speech recognition with small amounts of human-labeled speech data and large amounts of machine-labeled speech data.Semi-supervised learning is investigated to select the machine-transcribed training samples.Two semi-supervised learning methods are proposed: one is the local-global uncertainty based method which introduces both the local uncertainty from the current utterance and the global uncertainty from the whole data pool into the data selection; the other is the margin based data selection, which selects the utterances near to the decision boundary through language model tuning. The experimental results based on a Japanese far-field automatic speech recognition system indicate that the acoustic model trained by automatically transcribed speech data achieve about 17% relative gain when in-domain human annotated data was not available for initialization. While 3.7% relative gain was obtained when the initial acoustic model was trained by small amount of in-domain data.