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Distilling Knowledge from Ensembles of Neural Networks for Speech Recognition

Yevgen Chebotar
Interspeech (2016)
Google Scholar


Speech recognition systems that combine multiple types of acoustic models have been shown to outperform single-model systems. However, such systems can be complex to implement and too resource-intensive to use in production. This paper describes how to use knowledge distillation to combine acoustic models in a way that has the best of all worlds: It improves recognition accuracy significantly, can be implemented with standard training tools, and requires no additional complexity during recognition. First, we identify a simple but particularly strong type of ensemble: a late combination of recurrent neural networks with different architectures and training objectives. To harness such an ensemble, we use a variant of standard cross-entropy training to distill it into a single model and then discriminatively fine-tune the result. An evaluation on 2,000-hour large vocabulary tasks in 5 languages shows that the distilled models provide up to 8.9% WER improvement over conventionally-trained baselines, despite having an identical number of parameters.

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