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Enhancing Automatic Speech Recognition Through Integrated Noise Detection Architecture

Karamvir Singh

TL;DR

The paper tackles ASR performance degradation in noisy environments by integrating a noise-detection pathway into wav2vec2, forming a dual-head architecture that jointly optimizes transcription (via CTC loss) and noise classification (via cross-entropy loss). It systematically evaluates architectural variants using speech and environmental audio datasets, demonstrating near-perfect noise detection with Configurations B–D while preserving or improving transcription accuracy compared with a strong baseline. Key findings show that explicit noise handling enables effective joint optimization and that trainable loss balancing offers limited but positive gains, with fusion strategies providing marginal benefits. The work advances practical robust ASR by embedding signal-quality awareness into the recognition model, with implications for multilingual and adaptive-inference applications in challenging acoustic conditions.

Abstract

This research presents a novel approach to enhancing automatic speech recognition systems by integrating noise detection capabilities directly into the recognition architecture. Building upon the wav2vec2 framework, the proposed method incorporates a dedicated noise identification module that operates concurrently with speech transcription. Experimental validation using publicly available speech and environmental audio datasets demonstrates substantial improvements in transcription quality and noise discrimination. The enhanced system achieves superior performance in word error rate, character error rate, and noise detection accuracy compared to conventional architectures. Results indicate that joint optimization of transcription and noise classification objectives yields more reliable speech recognition in challenging acoustic conditions.

Enhancing Automatic Speech Recognition Through Integrated Noise Detection Architecture

TL;DR

The paper tackles ASR performance degradation in noisy environments by integrating a noise-detection pathway into wav2vec2, forming a dual-head architecture that jointly optimizes transcription (via CTC loss) and noise classification (via cross-entropy loss). It systematically evaluates architectural variants using speech and environmental audio datasets, demonstrating near-perfect noise detection with Configurations B–D while preserving or improving transcription accuracy compared with a strong baseline. Key findings show that explicit noise handling enables effective joint optimization and that trainable loss balancing offers limited but positive gains, with fusion strategies providing marginal benefits. The work advances practical robust ASR by embedding signal-quality awareness into the recognition model, with implications for multilingual and adaptive-inference applications in challenging acoustic conditions.

Abstract

This research presents a novel approach to enhancing automatic speech recognition systems by integrating noise detection capabilities directly into the recognition architecture. Building upon the wav2vec2 framework, the proposed method incorporates a dedicated noise identification module that operates concurrently with speech transcription. Experimental validation using publicly available speech and environmental audio datasets demonstrates substantial improvements in transcription quality and noise discrimination. The enhanced system achieves superior performance in word error rate, character error rate, and noise detection accuracy compared to conventional architectures. Results indicate that joint optimization of transcription and noise classification objectives yields more reliable speech recognition in challenging acoustic conditions.

Paper Structure

This paper contains 26 sections, 5 figures, 2 tables.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORK
  3. Noise Robustness in Speech Recognition
  4. Self-Supervised Speech Models
  5. Distinguishing Features of This Work
  6. PROPOSED METHODOLOGY
  7. Dataset Construction
  8. Speech Audio Collections
  9. Environmental Audio Collections
  10. Data Preprocessing
  11. Architectural Design
  12. Base Architecture Selection
  13. Noise Detection Integration
  14. Loss Function Design
  15. Feature Fusion Strategies
  16. ...and 11 more sections

Figures (5)

  • Figure 1: Standard wav2vec2 architecture without noise handling capabilities.
  • Figure 2: Comparison showing enhanced model's noise classification capability versus conventional approach.
  • Figure 3: Detailed architecture of wav2vec2 with integrated noise classification head.
  • Figure 4: Architecture incorporating adaptive loss weighting through trainable alpha parameter.
  • Figure 5: Advanced feature fusion architecture concatenating CNN positional encodings with transformer context representations.