90 research outputs found
On the Choice of Modeling Unit for Sequence-to-Sequence Speech Recognition
In conventional speech recognition, phoneme-based models outperform
grapheme-based models for non-phonetic languages such as English. The
performance gap between the two typically reduces as the amount of training
data is increased. In this work, we examine the impact of the choice of
modeling unit for attention-based encoder-decoder models. We conduct
experiments on the LibriSpeech 100hr, 460hr, and 960hr tasks, using various
target units (phoneme, grapheme, and word-piece); across all tasks, we find
that grapheme or word-piece models consistently outperform phoneme-based
models, even though they are evaluated without a lexicon or an external
language model. We also investigate model complementarity: we find that we can
improve WERs by up to 9% relative by rescoring N-best lists generated from a
strong word-piece based baseline with either the phoneme or the grapheme model.
Rescoring an N-best list generated by the phonemic system, however, provides
limited improvements. Further analysis shows that the word-piece-based models
produce more diverse N-best hypotheses, and thus lower oracle WERs, than
phonemic models.Comment: To appear in the proceedings of INTERSPEECH 201
Towards Language-Universal End-to-End Speech Recognition
Building speech recognizers in multiple languages typically involves
replicating a monolingual training recipe for each language, or utilizing a
multi-task learning approach where models for different languages have separate
output labels but share some internal parameters. In this work, we exploit
recent progress in end-to-end speech recognition to create a single
multilingual speech recognition system capable of recognizing any of the
languages seen in training. To do so, we propose the use of a universal
character set that is shared among all languages. We also create a
language-specific gating mechanism within the network that can modulate the
network's internal representations in a language-specific way. We evaluate our
proposed approach on the Microsoft Cortana task across three languages and show
that our system outperforms both the individual monolingual systems and systems
built with a multi-task learning approach. We also show that this model can be
used to initialize a monolingual speech recognizer, and can be used to create a
bilingual model for use in code-switching scenarios.Comment: submitted to ICASSP 201
Grapheme-to-Phoneme Conversion with Convolutional Neural Networks
Grapheme-to-phoneme (G2P) conversion is the process of generating pronunciation for words based on their written form. It has a highly essential role for natural language processing, text-to-speech synthesis and automatic speech recognition systems. In this paper, we investigate convolutional neural networks (CNN) for G2P conversion. We propose a novel CNN-based sequence-to-sequence (seq2seq) architecture for G2P conversion. Our approach includes an end-to-end CNN G2P conversion with residual connections, furthermore, a model, which utilizes a convolutional neural network (with and without residual connections) as encoder and Bi-LSTM as a decoder. We compare our approach with state-of-the-art methods, including Encoder-Decoder LSTM and Encoder-Decoder Bi-LSTM. Training and inference times, phoneme and word error rates were evaluated on the public CMUDict dataset for US English, and the best performing convolutional neural network based architecture was also evaluated on the NetTalk dataset. Our method approaches the accuracy of previous state-of-the-art results in terms of phoneme error rate
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