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Domain Generalization for Language-Independent Automatic Speech Recognition.

Heting Gao1, Junrui Ni1, Yang Zhang2

  • 1Department of Electrical and Computer Engineering (ECE), Beckman Institute, University of Illinois, Urbana, IL, United States.

Frontiers in Artificial Intelligence
|June 1, 2022
PubMed
Summary
This summary is machine-generated.

Achieving language-independent automatic speech recognition (ASR) depends on training data. Different strategies work best for broad vs. specialized ASR training sets, impacting phonetic transcription accuracy.

Keywords:
automatic speech recognitiondistributionally robust optimizationdomain generalizationinvariant risk minimizationregret minimizationunder-resourced languages

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Area of Science:

  • Speech Recognition
  • Computational Linguistics
  • Machine Learning

Background:

  • Language-independent automatic speech recognition (ASR) aims for phonetic transcription across diverse languages.
  • Training such systems is challenging due to language-specific phonetic implementations and articulatory features.

Purpose of the Study:

  • To explore different strategies for approximating language-independence in ASR.
  • To analyze the impact of training set size and language relationships on ASR performance.

Main Methods:

  • Investigated empirical risk minimization (e.g., connectionist temporal classification) for large, diverse training sets.
  • Evaluated domain-invariant representation learning strategies (invariant risk minimization, regret minimization) for smaller, related training sets.

Main Results:

  • Empirical risk minimization is effective when training sets include many languages and test languages are related.
  • Domain-invariant strategies excel when training sets are limited and test languages are unrelated.
  • Invariant risk minimization outperformed regret minimization in phone token classification.

Conclusions:

  • The optimal approach for language-independent ASR is contingent on the characteristics of the training and test data.
  • Representation learning strategies offer a promising avenue for improving ASR in low-resource or cross-family scenarios.