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Multi-Task Transformer with Adaptive Cross-Entropy Loss for Multi-Dialect Speech Recognition.

Zhengjia Dan1, Yue Zhao1, Xiaojun Bi1

  • 1School of Information Engineering, Minzu University of China, Beijing 100081, China.

Entropy (Basel, Switzerland)
|July 8, 2023
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Summary
This summary is machine-generated.

This study introduces a novel multi-dialect speech recognition model using soft-parameter-sharing and Transformer. It automatically balances tasks, significantly improving accuracy for Tibetan and Chinese dialects.

Keywords:
adaptive cross-entropy lossmulti-dialect speech recognitionmulti-task Transformer

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Area of Science:

  • Speech Recognition
  • Machine Learning
  • Natural Language Processing

Background:

  • Current multi-dialect speech recognition models often use hard-parameter sharing, obscuring task contributions.
  • Balancing multi-task learning typically requires manual, costly weight adjustments.

Purpose of the Study:

  • To develop an improved multi-dialect acoustic model.
  • To enable effective knowledge transfer between speech recognition and dialect identification tasks.
  • To automate the balancing of multi-task learning objectives.

Main Methods:

  • Proposed a multi-dialect acoustic model combining soft-parameter-sharing multi-task learning with Transformer architecture.
  • Introduced auxiliary cross-attentions for dialect ID recognition to inform speech recognition.
  • Employed an adaptive cross-entropy loss function for automatic multi-task weight balancing.

Main Results:

  • Significantly reduced syllable error rate for Tibetan multi-dialect speech recognition.
  • Significantly reduced character error rate for Chinese multi-dialect speech recognition.
  • Outperformed single-dialect, single-task multi-dialect, and hard parameter-sharing multi-task Transformers.

Conclusions:

  • The proposed soft-parameter-sharing approach effectively integrates dialect information.
  • Adaptive loss functions automate multi-task balancing, reducing manual effort.
  • The model demonstrates superior performance in low-resource and multi-dialect scenarios.