Ultra Fast Speech Separation Model with Teacher Student Learning

By Sanyuan Chen, Yu Wu, Zhuo Chen, Jian Wu, Takuya Yoshioka, Shujie Liu, Jinyu Li, Xiangzhan Yu. 2021

15 citations recorded by the source corpus.

Transformer has been successfully applied to speech separation recently with its strong long-dependency modeling capacity using a self-attention mechanism. However, Transformer tends to have heavy run-time costs due to the deep encoder layers, which hinders its deployment on edge devices. A small Transformer model with fewer encoder layers is preferred for computational efficiency, but it is prone to performance degradation. In this paper, an ultra fast speech separation Transformer model is proposed to achieve both better performance and efficiency with teacher student learning (T-S learning). We introduce layer-wise T-S learning and objective shifting mechanisms to guide the small student model to learn intermediate representations from the large teacher model. Compared with the small Transformer model trained from scratch, the proposed T-S learning method reduces the word error rate (WER) by more than 5% for both multi-channel and single-channel speech separation on LibriCSS dataset. Utilizing more unlabeled speech data, our ultra fast speech separation models achieve more than 10% relative WER reduction.

Subfields: Artificial Intelligence, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. Jinyu Li
  2. Zhuo Chen
  3. Takuya Yoshioka
  4. Shujie Liu
  5. Yu Wu
  6. Sanyuan Chen
  7. Jian Wu
  8. Naoyuki Kanda

In Signal Processing

  1. Jinyu Li
  2. Zhuo Chen
  3. Takuya Yoshioka
  4. Shujie Liu
  5. Yu Wu
  6. Sanyuan Chen
  7. Naoyuki Kanda
  8. Jian Wu

Citations in the corpus