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LID Models are Actually Accent Classifiers: Implications and Solutions for LID on Accented Speech

Niyati Bafna, Matthew Wiesner

TL;DR

The paper investigates why spoken language identification (LID) systems struggle with accented speech, revealing that accent-language confusion is a major error source when current systems rely on short phonotactic cues. The authors introduce sequence-level information via phoneme sequences (phoneseqs) and discrete SSL unit sequences (duseqs), integrating them with an ECAPA-TDNN backbone to form several ET+ variants and a fusion baseline. They show that accent-language confusion is mitigated by these sequence-level views, with ET+phoneseqs-train achieving substantial gains on L2 accents (e.g., up to +34 LID points for English L2) while keeping competitive performance on mainstream accents. The results suggest that combining acoustic and phonetic sequence information, along with input chunking, yields robust LID under accented speech and offers practical guidance for deploying LID systems in diverse linguistic environments.

Abstract

Prior research indicates that LID model performance significantly declines on accented speech; however, the specific causes, extent, and characterization of these errors remain under-explored. (i) We identify a common failure mode on accented speech whereby LID systems often misclassify L2 accented speech as the speaker's native language or a related language. (ii) We present evidence suggesting that state-of-the-art models are invariant to permutations of short spans of speech, implying they classify on the basis of short phonotactic features indicative of accent rather than language. Our analysis reveals a simple method to enhance model robustness to accents through input chunking. (iii) We present an approach that integrates sequence-level information into our model without relying on monolingual ASR systems; this reduces accent-language confusion and significantly enhances performance on accented speech while maintaining comparable results on standard LID.

LID Models are Actually Accent Classifiers: Implications and Solutions for LID on Accented Speech

TL;DR

The paper investigates why spoken language identification (LID) systems struggle with accented speech, revealing that accent-language confusion is a major error source when current systems rely on short phonotactic cues. The authors introduce sequence-level information via phoneme sequences (phoneseqs) and discrete SSL unit sequences (duseqs), integrating them with an ECAPA-TDNN backbone to form several ET+ variants and a fusion baseline. They show that accent-language confusion is mitigated by these sequence-level views, with ET+phoneseqs-train achieving substantial gains on L2 accents (e.g., up to +34 LID points for English L2) while keeping competitive performance on mainstream accents. The results suggest that combining acoustic and phonetic sequence information, along with input chunking, yields robust LID under accented speech and offers practical guidance for deploying LID systems in diverse linguistic environments.

Abstract

Prior research indicates that LID model performance significantly declines on accented speech; however, the specific causes, extent, and characterization of these errors remain under-explored. (i) We identify a common failure mode on accented speech whereby LID systems often misclassify L2 accented speech as the speaker's native language or a related language. (ii) We present evidence suggesting that state-of-the-art models are invariant to permutations of short spans of speech, implying they classify on the basis of short phonotactic features indicative of accent rather than language. Our analysis reveals a simple method to enhance model robustness to accents through input chunking. (iii) We present an approach that integrates sequence-level information into our model without relying on monolingual ASR systems; this reduces accent-language confusion and significantly enhances performance on accented speech while maintaining comparable results on standard LID.

Paper Structure

This paper contains 15 sections, 2 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Datasets
  4. Models
  5. Baselines and Analysis
  6. Using phone transcripts
  7. Using discrete SSL units
  8. Error profiles on accented speech
  9. Accent-language confusion
  10. Explaining accent-language confusion
  11. Block Permutation Invariance
  12. Long Range Dependency
  13. Results and Discussion
  14. Conclusion
  15. Acknowledgments

Figures (2)

  • Figure 1: The depicted model augments the ECAPA-TDNN representation with one produced by passing a quantized representation of speech into a learned transformer model. ET+phoneseqs-train uses a phonetic transcript of the audio, while ET+duseqsembed-train quantizes SSL representations with K-means clustering. For ET+duseqs-train, the transformer embedding layer is initialized as the K-means centroids. The classifier produces scores $s\left(l_i\right)$ for each language, $l_i$, among $N$ possibilities.
  • Figure 2: Error profiles for SOTA (1, 2) and our best-performing ET+phoneseqs-train model (5) on EdAcc accents, showing the top 3 languages that each accent was mis-classified as, as well as associated percentage of total error. (3) and (4) show the error profile for phoneseqs and ET+phoneseqs-train on samples where ECAPA-TDNN made mistakes. Errors indicative of accent-language confusion are highlighted in green.