End-To-End Speech Recognition Using A High Rank LSTM-CTC Based Model
This is an incremental improvement for speech recognition systems, enhancing model expressiveness without external data.
The paper tackles the problem of improving speech recognition accuracy by replacing the bottleneck projection matrix in LSTM-CTC models with a high-rank projection layer, resulting in a 4%-6% relative word error rate reduction on WSJ and LibriSpeech datasets.
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.