Phoneme Based Neural Transducer for Large Vocabulary Speech Recognition

• URL: http://arxiv.org/abs/2010.16368v4
• Date: Tue, 20 Apr 2021 13:05:56 GMT
• Title: Phoneme Based Neural Transducer for Large Vocabulary Speech Recognition
• Authors: Wei Zhou and Simon Berger and Ralf Schl\"uter and Hermann Ney
• Abstract summary: We present a simple, novel and competitive approach for phoneme-based neural transducer modeling. A phonetic context size of one is shown to be sufficient for the best performance. The overall performance of our best model is comparable to state-of-the-art (SOTA) results for the TED-LIUM Release 2 and Switchboard corpora.
• Score: 41.92991390542083
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To join the advantages of classical and end-to-end approaches for speech recognition, we present a simple, novel and competitive approach for phoneme-based neural transducer modeling. Different alignment label topologies are compared and word-end-based phoneme label augmentation is proposed to improve performance. Utilizing the local dependency of phonemes, we adopt a simplified neural network structure and a straightforward integration with the external word-level language model to preserve the consistency of seq-to-seq modeling. We also present a simple, stable and efficient training procedure using frame-wise cross-entropy loss. A phonetic context size of one is shown to be sufficient for the best performance. A simplified scheduled sampling approach is applied for further improvement and different decoding approaches are briefly compared. The overall performance of our best model is comparable to state-of-the-art (SOTA) results for the TED-LIUM Release 2 and Switchboard corpora.

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