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arxiv: 2605.12225 · v1 · submitted 2026-05-12 · 💻 cs.CL

Recognition: no theorem link

Mechanistic Interpretability of ASR models using Sparse Autoencoders

Dan Pluth , Zachary Nicholas Houghton , Yu Zhou , Vijay K. Gurbani
Authors on Pith no claims yet

Pith reviewed 2026-05-13 05:31 UTC · model grok-4.3

classification 💻 cs.CL
keywords sparse autoencodersmechanistic interpretabilityWhisperautomatic speech recognitionmonosemantic featurescross-lingual steeringTransformer embeddings
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The pith

Sparse autoencoders recover monosemantic features from Whisper's internal speech representations.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper trains a sparse autoencoder on frame-level embeddings taken from the encoder of the Whisper automatic speech recognition model. It shows that the resulting sparse latent space contains features tied to specific linguistic elements as well as non-linguistic audio properties. A reader would care because the same method previously used on text models now appears to work on audio models, making it possible to inspect and steer what the system has learned about language and sound. The authors further report that these features support cross-lingual steering, which indicates that Whisper stores linguistic information in a form that can be isolated and manipulated.

Core claim

Training a sparse autoencoder on frame-level embeddings from Whisper's encoder yields a high-dimensional sparse latent space populated by diverse monosemantic features that cross linguistic and non-linguistic boundaries. These features enable cross-lingual steering and demonstrate that Whisper internally encodes a rich amount of linguistic information.

What carries the argument

Sparse autoencoder trained on frame-level embeddings from the Whisper encoder that projects dense vectors into a sparse space to isolate monosemantic features.

Load-bearing premise

The sparse features recovered by the autoencoder correspond to genuine distinct concepts inside the Whisper model rather than incidental patterns or training artifacts.

What would settle it

If steering or ablating an identified feature for a specific phoneme, word, or language does not produce the expected selective change in the model's transcription output on held-out audio, the interpretation would be falsified.

Figures

Figures reproduced from arXiv: 2605.12225 by Dan Pluth, Vijay K. Gurbani, Yu Zhou, Zachary Nicholas Houghton.

Figure 1
Figure 1. Figure 1: Diagram of the Whisper architecture. The SAE is trained based on the outputs [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The top 5 words with the high￾est average number of frames with latent index 6373 activated (precision value of 88.7%, recall value of 64.9%). The dif￾ferent colored bars represent the aver￾age amount of activated frames that each phone in the word received. Phones ab￾sent in plot were not activated for this latent [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: shows the confusion matrix created using this predictor to compare to the ground truth. This predictor has a 97.7% precision for the noise audio samples [PITH_FULL_IMAGE:figures/full_fig_p015_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Three different positional latents showing activations on 20 different audio files. [PITH_FULL_IMAGE:figures/full_fig_p016_5.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript applies Sparse Autoencoders (SAEs) to frame-level embeddings extracted from the encoder of the Whisper ASR model. It reports the recovery of diverse monosemantic features spanning linguistic and non-linguistic categories and demonstrates cross-lingual feature steering via these features. The central claim is that this establishes the viability of SAEs for mechanistic interpretability of ASR models and shows that Whisper encodes substantial linguistic information.

Significance. If the empirical results can be placed on a firmer quantitative footing, the work would constitute a useful first extension of SAE-based interpretability techniques from text LLMs to the audio domain. It could supply new diagnostic and intervention tools for speech models that are already deployed at scale.

major comments (3)
  1. [Abstract] Abstract: the assertion that the SAE 'uncovers diverse monosemantic features' and enables 'cross-lingual feature steering' is presented without any quantitative metrics, activation statistics, ablation controls, or causal intervention results. This leaves the central claim vulnerable to the alternative explanation that the recovered directions simply reflect statistical regularities in the training audio rather than causally relevant internal representations of Whisper.
  2. [Results / Experiments] The description of cross-lingual steering does not report controls that would isolate the effect of the target feature from generic activation scaling or from correlations already present in the embedding distribution; without such controls the steering result remains compatible with non-model-specific explanations.
  3. [Methods / Experiments] No ablation studies, baseline comparisons (e.g., random directions or PCA), or error analysis are described that would quantify how faithfully the SAE features correspond to Whisper's computations versus training artifacts of the SAE optimizer itself.
minor comments (2)
  1. [Abstract] The abstract would be clearer if it stated the SAE dictionary size, sparsity penalty, and training corpus size.
  2. [Figures] Figure captions and axis labels should explicitly indicate whether activations are normalized or raw and what the color scale represents.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback. We agree that additional quantitative metrics, controls for steering, and ablations would place the empirical results on firmer ground and better distinguish model-specific representations from artifacts. We will revise the manuscript to incorporate these elements while preserving the core contribution as an initial extension of SAE techniques to ASR models.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the assertion that the SAE 'uncovers diverse monosemantic features' and enables 'cross-lingual feature steering' is presented without any quantitative metrics, activation statistics, ablation controls, or causal intervention results. This leaves the central claim vulnerable to the alternative explanation that the recovered directions simply reflect statistical regularities in the training audio rather than causally relevant internal representations of Whisper.

    Authors: We acknowledge that the abstract and results presentation rely primarily on qualitative examples. In the revision we will add quantitative support including feature activation frequencies, sparsity levels, and reconstruction statistics. We will also expand the description of steering interventions with effect sizes to better support causal relevance to Whisper's internal computations. revision: yes

  2. Referee: [Results / Experiments] The description of cross-lingual steering does not report controls that would isolate the effect of the target feature from generic activation scaling or from correlations already present in the embedding distribution; without such controls the steering result remains compatible with non-model-specific explanations.

    Authors: We agree that isolating feature-specific effects requires explicit controls. The revised manuscript will include baseline interventions using random directions and uniform scaling of activations, allowing direct comparison to demonstrate that steering outcomes are attributable to the learned SAE features rather than generic embedding properties. revision: yes

  3. Referee: [Methods / Experiments] No ablation studies, baseline comparisons (e.g., random directions or PCA), or error analysis are described that would quantify how faithfully the SAE features correspond to Whisper's computations versus training artifacts of the SAE optimizer itself.

    Authors: We recognize the importance of these validations. We will add ablation experiments on SAE hyperparameters, direct comparisons of feature interpretability against PCA and random vectors, and reconstruction error analysis to quantify fidelity to Whisper's encoder representations versus SAE training artifacts. revision: yes

Circularity Check

0 steps flagged

Empirical SAE application to Whisper embeddings contains no circular derivation steps

full rationale

The paper describes training a sparse autoencoder on frame-level activations extracted from the Whisper encoder and then inspecting the resulting features for monosemanticity and steering effects. No equations, uniqueness theorems, or parameter-fitting procedures are presented that reduce the central claims (discovery of linguistic features, cross-lingual steering) back to the inputs by construction. The work is a direct empirical application of an existing technique; the reader's assessment that no self-referential definitions or fitted-input predictions exist is consistent with the abstract and described methodology.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract supplies no explicit free parameters, axioms, or invented entities. The central claim rests on the unstated assumption that standard SAE training will isolate interpretable units in ASR embeddings.

pith-pipeline@v0.9.0 · 5483 in / 1032 out tokens · 132301 ms · 2026-05-13T05:31:26.273445+00:00 · methodology

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