Hybrid CTC-Attention based End-to-End Speech Recognition using Subword Units.
2018 11th International Symposium on Chinese Spoken Language Processing (ISCSLP)(2018)
摘要
In this paper, we present an end-to-end automatic speech recognition system, which successfully employs subword units in a hybrid CTC-Attention based system. The subword units are obtained by the byte-pair encoding (BPE) compression algorithm. Compared to using words as modeling units, using characters or subword units does not suffer from the out-of-vocabulary (OOV) problem. Furthermore, using subword units further offers a capability in modeling longer context than using characters. We evaluate different systems over the LibriSpeech 1000h dataset. The subword-based hybrid CTC-Attention system obtains 6.8% word error rate (WER) on the test clean subset without any dictionary or external language model. This represents a significant improvement (a 12.8% WER relative reduction) over the character-based hybrid CTC-Attention system.
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关键词
Decoding,Speech recognition,Vocabulary,Acoustics,Computer architecture,Encoding,Dictionaries
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