Recently, end-to-end speech recognition with a hybrid model consisting of the connectionist temporal classification(CTC) and the attention encoder-decoder achieved state-of-the-art results. In this paper, we propose a novel CTC decoder structure based on the experiments we conducted and explore the relation between decoding performance and the depth of encoder. We also apply attention smoothing mechanism to acquire more context information for subword-based decoding. Taken together, these strategies allow us to achieve a word error rate(WER) of 4.43 without LM and 3.34 with RNN-LM on the test-clean subset of the LibriSpeech corpora, which by far are the best reported WERs for end-to-end ASR systems on this dataset.
Methods based on large lexicon, such as phoneme-based ASR systems or word-based ASR systems, are not able to resolve out-of-vocabulary (OOV) problems. Thus, starting from LAS @cite_1 , such seq2seq model raises new character-based method. By combining frame information in audio clips and the corresponding characters together, the OOV problem is resolved to some extent. Since many characters in English words are silent and same characters in different sentences may pronounce differently (e.g. "a" in "apple" and "approve"), decoding procedure on character level relies heavily on the sentence sequence relationship given by RNN rather than the acoustic information given by the audio clip frames, which results in the uncertainty of decoding procedure on character level. Considering all the issues mentioned above, subword-based structure can resolve OOV problems on one hand, and can learn the relationship between acoustic information and character information on the other hand. An effective and fast method for generating subwords is byte-pair encoding (BPE) @cite_7 . Which is a compression algorithm that iteratively replaces the most frequent pair of units (or bytes) with an unused unit, and eventually generates new units that are consistent with the number of iterations.
@cite_1 @cite_7
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We present Listen, Attend and Spell (LAS), a neural speech recognizer that transcribes speech utterances directly to characters without pronunciation models, HMMs or other components of traditional speech recognizers. In LAS, the neural network architecture subsumes the acoustic, pronunciation and language models making it not only an end-to-end trained system but an end-to-end model. In contrast to DNN-HMM, CTC and most other models, LAS makes no independence assumptions about the probability distribution of the output character sequences given the acoustic sequence. Our system has two components: a listener and a speller. The listener is a pyramidal recurrent network encoder that accepts filter bank spectra as inputs. The speller is an attention-based recurrent network decoder that emits each character conditioned on all previous characters, and the entire acoustic sequence. On a Google voice search task, LAS achieves a WER of 14.1 without a dictionary or an external language model and 10.3 with language model rescoring over the top 32 beams. In comparison, the state-of-the-art CLDNN-HMM model achieves a WER of 8.0 on the same set. Neural machine translation (NMT) models typically operate with a fixed vocabulary, but translation is an open-vocabulary problem. Previous work addresses the translation of out-of-vocabulary words by backing off to a dictionary. In this paper, we introduce a simpler and more effective approach, making the NMT model capable of open-vocabulary translation by encoding rare and unknown words as sequences of subword units. This is based on the intuition that various word classes are translatable via smaller units than words, for instance names (via character copying or transliteration), compounds (via compositional translation), and cognates and loanwords (via phonological and morphological transformations). We discuss the suitability of different word segmentation techniques, including simple character n-gram models and a segmentation based on the byte pair encoding compression algorithm, and empirically show that subword models improve over a back-off dictionary baseline for the WMT 15 translation tasks English-German and English-Russian by 1.1 and 1.3 BLEU, respectively.