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Towards Acoustic Model Unification Across Dialects

Meysam Bastani
Mohamed G. Elfeky
Pedro Moreno
2016 IEEE Workshop on Spoken Language Technology


Research has shown that acoustic model performance typically decreases when evaluated on a dialectal variation of the same language that was not used during training. Similarly, models simultaneously trained on a group of dialects tend to under-perform when compared to dialect-specific models. In this paper, we report on our efforts towards building a unified acoustic model that can serve a multi-dialectal language. Two techniques are presented: Distillation and MTL. In Distillation, we use an ensemble of dialect-specific acoustic models and distill its knowledge in a single model. In MTL, we utilize MultiTask Learning to train a unified acoustic model that learns to distinguish dialects as a side task. We show that both techniques are superior to the naive model that is trained on all dialectal data, reducing word error rates by 4.2% and 0.6%, respectively. And, while achieving this improvement, neither technique degrades the performance of the dialect-specific models by more than 3.4%.

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