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Rethinking Processing Distortions: Disentangling the Impact of Speech Enhancement Errors on Speech Recognition Performance

Tsubasa Ochiai, Kazuma Iwamoto, Marc Delcroix, Rintaro Ikeshita, Hiroshi Sato, Shoko Araki, Shigeru Katagiri

TL;DR

This work addresses why single-channel SE front-ends often fail to boost ASR in noise by introducing an orthogonal projection-based error decomposition and a direct scaling analysis (DSA) that attributes ASR degradation primarily to artifact errors, rather than residual interference or noise. It then proposes two practical mitigations—observation adding (OA) post-processing and an artifact-aware AB-SDR training objective—that reduce artifact errors and yield substantial ASR gains across single- and multi-talker conditions and real recordings. The study validates the approach across multiple datasets and SE/ASR configurations, providing both theoretical justification (for OA) and empirical evidence (for OA and AB-SDR) that mitigating artifact errors improves WER. Overall, the paper offers a principled framework for diagnosing and mitigating processing distortions in single-channel SE to enhance ASR robustness, with implications for SE front-end design and modular ASR pipelines.

Abstract

It is challenging to improve automatic speech recognition (ASR) performance in noisy conditions with a single-channel speech enhancement (SE) front-end. This is generally attributed to the processing distortions caused by the nonlinear processing of single-channel SE front-ends. However, the causes of such degraded ASR performance have not been fully investigated. How to design single-channel SE front-ends in a way that significantly improves ASR performance remains an open research question. In this study, we investigate a signal-level numerical metric that can explain the cause of degradation in ASR performance. To this end, we propose a novel analysis scheme based on the orthogonal projection-based decomposition of SE errors. This scheme manually modifies the ratio of the decomposed interference, noise, and artifact errors, and it enables us to directly evaluate the impact of each error type on ASR performance. Our analysis reveals the particularly detrimental effect of artifact errors on ASR performance compared to the other types of errors. This provides us with a more principled definition of processing distortions that cause the ASR performance degradation. Then, we study two practical approaches for reducing the impact of artifact errors. First, we prove that the simple observation adding (OA) post-processing (i.e., interpolating the enhanced and observed signals) can monotonically improve the signal-to-artifact ratio. Second, we propose a novel training objective, called artifact-boosted signal-to-distortion ratio (AB-SDR), which forces the model to estimate the enhanced signals with fewer artifact errors. Through experiments, we confirm that both the OA and AB-SDR approaches are effective in decreasing artifact errors caused by single-channel SE front-ends, allowing them to significantly improve ASR performance.

Rethinking Processing Distortions: Disentangling the Impact of Speech Enhancement Errors on Speech Recognition Performance

TL;DR

This work addresses why single-channel SE front-ends often fail to boost ASR in noise by introducing an orthogonal projection-based error decomposition and a direct scaling analysis (DSA) that attributes ASR degradation primarily to artifact errors, rather than residual interference or noise. It then proposes two practical mitigations—observation adding (OA) post-processing and an artifact-aware AB-SDR training objective—that reduce artifact errors and yield substantial ASR gains across single- and multi-talker conditions and real recordings. The study validates the approach across multiple datasets and SE/ASR configurations, providing both theoretical justification (for OA) and empirical evidence (for OA and AB-SDR) that mitigating artifact errors improves WER. Overall, the paper offers a principled framework for diagnosing and mitigating processing distortions in single-channel SE to enhance ASR robustness, with implications for SE front-end design and modular ASR pipelines.

Abstract

It is challenging to improve automatic speech recognition (ASR) performance in noisy conditions with a single-channel speech enhancement (SE) front-end. This is generally attributed to the processing distortions caused by the nonlinear processing of single-channel SE front-ends. However, the causes of such degraded ASR performance have not been fully investigated. How to design single-channel SE front-ends in a way that significantly improves ASR performance remains an open research question. In this study, we investigate a signal-level numerical metric that can explain the cause of degradation in ASR performance. To this end, we propose a novel analysis scheme based on the orthogonal projection-based decomposition of SE errors. This scheme manually modifies the ratio of the decomposed interference, noise, and artifact errors, and it enables us to directly evaluate the impact of each error type on ASR performance. Our analysis reveals the particularly detrimental effect of artifact errors on ASR performance compared to the other types of errors. This provides us with a more principled definition of processing distortions that cause the ASR performance degradation. Then, we study two practical approaches for reducing the impact of artifact errors. First, we prove that the simple observation adding (OA) post-processing (i.e., interpolating the enhanced and observed signals) can monotonically improve the signal-to-artifact ratio. Second, we propose a novel training objective, called artifact-boosted signal-to-distortion ratio (AB-SDR), which forces the model to estimate the enhanced signals with fewer artifact errors. Through experiments, we confirm that both the OA and AB-SDR approaches are effective in decreasing artifact errors caused by single-channel SE front-ends, allowing them to significantly improve ASR performance.
Paper Structure (41 sections, 1 theorem, 12 equations, 7 figures, 6 tables, 1 algorithm)

This paper contains 41 sections, 1 theorem, 12 equations, 7 figures, 6 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Hypotheses on cause of ASR performance degradation
  3. Contributions: Experimental proof of impact of artifact errors and practical approaches to mitigate them
  4. Related works
  5. Observation adding
  6. Artifact-aware training objective
  7. ASR-level training objectives
  8. Background
  9. Overview of speech enhancement task
  10. Orthogonal projection-based decomposition of speech enhancement errors
  11. Evaluation measures
  12. Proposed analyses and approaches to reducing artifact errors
  13. Controlling SIR, SNR, and SAR by directly scaling error components
  14. Improving SAR with observation adding post-processing
  15. Formulation
  16. ...and 26 more sections

Key Result

Proposition 1

The OA operation in Eq eq:oa_int improves the SAR of the original enhanced signal $\widehat{\mathbf{s}} = \mathrm{SE}(\mathbf{y})$ if it satisfies $\langle \widehat{\mathbf{s}}, \mathbf{y} \rangle > 0$.

Figures (7)

  • Figure 1: Illustrations of (a) orthogonal projection-based decomposition and (b) effect of observation adding from viewpoint of orthogonal projection-based decomposition.
  • Figure 2: Results of DSA-based evaluation (WER [%] (lower is better)) for single-talker setup (WSJ_CHIME_ST), which modifies the scale of noise ($\omega_{\text{noise}}$) and artifact ($\omega_{\text{artif}}$) error components.
  • Figure 3: Results of DSA-based evaluation (WER [%] (lower is better)) for multi-talker setup (WSJ_CHIME_MT), which modifies the scale of interference ($\omega_{\text{interf}}$), noise ($\omega_{\text{noise}}$), and artifact ($\omega_{\text{artif}}$) error components.
  • Figure 4: Results of OA post-processing (SDR, SNR, SAR [dB] (higher is better) and WER [%] (lower is better)) for single-talker setup (WSJ_CHIME_ST). We use the compact notation SXR, in place of SDR, SNR, and SAR, to denote the ratio between two signals S and X, where X is either the distortion, noise, or artifacts.
  • Figure 5: Results of OA post-processing (SDR, SIR, SNR, SAR [dB] (higher is better) and WER [%] (lower is better)) for multi-talker setup (WSJ_CHIME_MT). SXR denotes values encompassing SDR, SIR, SNR, and SAR.
  • ...and 2 more figures

Theorems & Definitions (2)

  • Proposition 1
  • proof