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Discrete Speech Unit Extraction via Independent Component Analysis

Tomohiko Nakamura, Kwanghee Choi, Keigo Hojo, Yoshiaki Bando, Satoru Fukayama, Shinji Watanabe

TL;DR

The paper addresses how linear preprocessing before k-means affects discrete speech unit extraction from self-supervised speech representations. It evaluates standardization, PCA, whitening, and ICA on DSU benchmarks, finding that whitening and ICA—especially ICA combined with Cosine distance—improve ASR performance and reduce DSU bit-rates. Qualitative analyses show that preprocessing fosters isotropy among DSU centroids and that ICA components yield linguistically interpretable phonetic contrasts and allophonic distinctions. This suggests that simple linear transforms can meaningfully enhance DSU-based ASR pipelines and provide deeper insights into the geometry of S3M representations.

Abstract

Self-supervised speech models (S3Ms) have become a common tool for the speech processing community, leveraging representations for downstream tasks. Clustering S3M representations yields discrete speech units (DSUs), which serve as compact representations for speech signals. DSUs are typically obtained by k-means clustering. Using DSUs often leads to strong performance in various tasks, including automatic speech recognition (ASR). However, even with the high dimensionality and redundancy of S3M representations, preprocessing S3M representations for better clustering remains unexplored, even though it can affect the quality of DSUs. In this paper, we investigate the potential of linear preprocessing methods for extracting DSUs. We evaluate standardization, principal component analysis, whitening, and independent component analysis (ICA) on DSU-based ASR benchmarks and demonstrate their effectiveness as preprocessing for k-means. We also conduct extensive analyses of their behavior, such as orthogonality or interpretability of individual components of ICA.

Discrete Speech Unit Extraction via Independent Component Analysis

TL;DR

The paper addresses how linear preprocessing before k-means affects discrete speech unit extraction from self-supervised speech representations. It evaluates standardization, PCA, whitening, and ICA on DSU benchmarks, finding that whitening and ICA—especially ICA combined with Cosine distance—improve ASR performance and reduce DSU bit-rates. Qualitative analyses show that preprocessing fosters isotropy among DSU centroids and that ICA components yield linguistically interpretable phonetic contrasts and allophonic distinctions. This suggests that simple linear transforms can meaningfully enhance DSU-based ASR pipelines and provide deeper insights into the geometry of S3M representations.

Abstract

Self-supervised speech models (S3Ms) have become a common tool for the speech processing community, leveraging representations for downstream tasks. Clustering S3M representations yields discrete speech units (DSUs), which serve as compact representations for speech signals. DSUs are typically obtained by k-means clustering. Using DSUs often leads to strong performance in various tasks, including automatic speech recognition (ASR). However, even with the high dimensionality and redundancy of S3M representations, preprocessing S3M representations for better clustering remains unexplored, even though it can affect the quality of DSUs. In this paper, we investigate the potential of linear preprocessing methods for extracting DSUs. We evaluate standardization, principal component analysis, whitening, and independent component analysis (ICA) on DSU-based ASR benchmarks and demonstrate their effectiveness as preprocessing for k-means. We also conduct extensive analyses of their behavior, such as orthogonality or interpretability of individual components of ICA.
Paper Structure (13 sections, 10 equations, 4 figures, 3 tables)

This paper contains 13 sections, 10 equations, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Linear Preprocessing Methods for Discrete Speech Unit Extraction
  3. $k$-means and Linear Transformation
  4. Linear Preprocessing Methods
  5. Experimental Results
  6. Experimental Setup
  7. Effects of different preprocessing techniques
  8. Effect of the number of clusters
  9. Effects of S3M characteristics
  10. Qualitative Analysis of Clustering Results
  11. Orthogonality of $k$-means centroids
  12. Interpretability of $k$-means centroids and ICA components
  13. Conclusion

Figures (4)

  • Figure 1: Schematic illustration of processing flow for DSU-based ASR model. Our work focuses on improving the preprocessing step for $k$-means (bold).
  • Figure 2: Comparing cosine similarities and their averages between $k$-means cluster centroids. Different preprocessing methods are used before clustering.
  • Figure 3: Comparing $k$-means centroids with phones with $k=100$. We plot the centroids where the 10 nearest neighbor representations are from the same phone. Due to space limitations, we plot stops and fricatives only.
  • Figure 4: Comparing ICA components with phones where number of components is 100. We plot the components where the top 5 and bottom 5 representations are from the same phone.