Multi-Head Decoder for End-to-End Speech Recognition
Table 1: Experimental conditions.
['# training', '445,068 utterances (581 hours)']
To evaluate the performance of our proposed method, we conducted experimental evaluation using Corpus of Spontaneous Japanese (CSJ) To compare the performance, we used following dot, additive, location, and three variants of multi-head attention methods: Dot-product attention-based model (Dot), Additive attention-based model (Add), Location-aware attention-based model (Loc), Multi-head dot-product attention model (MHA-Dot), Multi-head additive attention model (MHA-Add), Multi-head location attention model (MHA-Loc). (BLSTMP) and one-layered LSTM, respectively. In the second and third bottom layers in the encoder, subsampling was performed to reduce the length of utterance, yielding the length T/4. For MHA/MHD, we set the number of heads to four. For HMHD, we used two kind of settings: (1) dot-product attention + additive attention + location-based attention + coverage mechanism attention (Dot+Add+Loc+Cov), and (2) two location-based attentions + two coverage mechanism attentions (2×Loc+2×Cov). The number of distinct output characters was 3,315 including Kanji, Hiragana, Katakana, alphabets, Arabic number and sos/eos symbols. We manually set maximum and minimum lengths of the output sequence to 0.1 and 0.5 times the length of the subsampled input sequence, respectively, and the length penalty to 0.1 times the length of the output sequence. All of the networks were trained using end-to-end speech processing toolkit ESPnet Character error rate (CER) was used as a metric.