Mechanistic Interpretability of ASR models using Sparse Autoencoders

2026-05-12

AI Summary

This work addresses the limited understanding of internal representation mechanisms in automatic speech recognition (ASR) models by introducing sparse autoencoders (SAEs) to analyze frame-level encoder embeddings of the Whisper model. For the first time, SAEs are employed to construct a high-dimensional sparse latent space that disentangles the semantic structure embedded within Whisper’s representations. The experiments demonstrate that SAEs effectively extract monosemantic features spanning both linguistic and non-linguistic boundaries and enable controllable cross-lingual interventions. These findings not only validate the feasibility of applying SAEs to interpret audio foundation models but also reveal that Whisper encodes rich, structured linguistic information. This study thus establishes a novel paradigm for enhancing the interpretability of ASR systems through sparse representation learning.

Abstract

Understanding the internal machinations of deep Transformer-based NLP models is more crucial than ever as these models see widespread use in various domains that affect the public at large, such as industry, academia, finance, health. While these models have advanced rapidly, their internal mechanisms remain largely a mystery. Techniques such as Sparse Autoencoders (SAE) have emerged to understand these mechanisms by projecting dense representations into a sparse vector. While existing research has demonstrated the viability of the SAE in interpreting text-based Large Language Models (LLMs), there are no equivalent studies that demonstrate the application of a SAE to audio processing models like Automatic Speech Recognizers (ASRs). In this work, a SAE is applied to Whisper, a Transformer-based ASR, training a high-dimensional sparse latent space on frame-level embeddings extracted from the Whisper encoder. Our work uncovers diverse monosemantic features across linguistic and non-linguistic boundaries, and demonstrates cross-lingual feature steering. This work establishes the viability of a SAE model and demonstrates that Whisper encodes a rich amount of linguistic information.

Problem

• Mechanistic Interpretability
• Automatic Speech Recognition
• Sparse Autoencoders
• Transformer-based ASR
• Whisper

Innovation

• Sparse Autoencoder
• Mechanistic Interpretability
• Automatic Speech Recognition
• Whisper
• Cross-lingual Feature Steering