prabhavalkar17@interspeech_2017@ISCA

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#1 A Comparison of Sequence-to-Sequence Models for Speech Recognition [PDF] [Copy] [Kimi] [REL]

Authors: Rohit Prabhavalkar, Kanishka Rao, Tara N. Sainath, Bo Li, Leif Johnson, Navdeep Jaitly

In this work, we conduct a detailed evaluation of various all-neural, end-to-end trained, sequence-to-sequence models applied to the task of speech recognition. Notably, each of these systems directly predicts graphemes in the written domain, without using an external pronunciation lexicon, or a separate language model. We examine several sequence-to-sequence models including connectionist temporal classification (CTC), the recurrent neural network (RNN) transducer, an attention-based model, and a model which augments the RNN transducer with an attention mechanism. We find that the sequence-to-sequence models are competitive with traditional state-of-the-art approaches on dictation test sets, although the baseline, which uses a separate pronunciation and language model, outperforms these models on voice-search test sets.

Subject: INTERSPEECH.2017 - Speech Recognition