End-To-End Speech Recognition Using A High Rank LSTM-CTC Based Model

Yangyang Shi; Mei-Yuh Hwang; Xin Lei

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arXiv · 1903.05261

End-To-End Speech Recognition Using A High Rank LSTM-CTC Based Model

Yangyang Shi·Mei-Yuh Hwang·Xin Lei

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Abstract

Long Short Term Memory Connectionist Temporal Classification (LSTM-CTC) based end-to-end models are widely used in speech recognition due to its simplicity in training and efficiency in decoding. In conventional LSTM-CTC based models, a bottleneck projection matrix maps the hidden feature vectors obtained from LSTM to softmax output layer. In this paper, we propose to use a high rank projection layer to replace the projection matrix. The output from the high rank projection layer is a weighted combination of vectors that are projected from the hidden feature vectors via different projection matrices and non-linear activation function. The high rank projection layer is able to improve the expressiveness of LSTM-CTC models. The experimental results show that on Wall Street Journal (WSJ) corpus and LibriSpeech data set, the proposed method achieves 4%-6% relative word error rate (WER) reduction over the baseline CTC system. They outperform other published CTC based end-to-end (E2E) models under the condition that no external data or data augmentation is applied. Code has been made available at https://github.com/mobvoi/lstm_ctc.

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BibTeX RIS

Yangyang Shi, Mei-Yuh Hwang, Xin Lei. 2019-03-12. End-To-End Speech Recognition Using A High Rank LSTM-CTC Based Model. https://arxiv.org/abs/1903.05261

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