Post-hoc Disentanglement of Textual and Acoustic Features in Pre-trained Speech Encoders

arXiv:2410.03037 · cs.CL, cs.LG, cs.SD, eess.AS · Submitted 2024-10-03 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: We're looking at the paper: "Post-hoc Disentanglement of Textual and Acoustic Features in Pre-trained Speech Encoders".

Jane: The gist The framework proposes a two-stage disentanglement approach based on the Information Bottleneck principle to separate complex speech representations into distinct textual and acoustic components for downstream tasks like…

Tom: First, who's behind it and why it matters.

Paper summary: Tom: So we're looking at this paper called "Post-hoc Disentanglement of Textual and Acoustic Features in Pre-trained Speech Encoders". The main idea here is that these deep models, these pre-trained speech encoders, they build up really tangled internal representations that capture a lot of stuff at once.

Jane: Right, so it's like the model mixes up things like pitch and loudness with the actual words or meaning in a way that makes it hard to tell what's coming from text versus what's just acoustic sound.

Tom: Exactly. And this entanglement makes it really tricky to track how much of that representation relies on the text versus just the sound when you use it for something else, like recognizing emotion or identifying a speaker.

Lu: The authors build on the Information Bottleneck principle to propose a two-stage disentanglement framework. They take these pre-trained speech representations and separate them into two distinct parts.

Meng: So they are trying to force the model to split those complex representations into one part that is just text and another part that is acoustic features relevant for a specific task.

Lalam: Basically, Stage one learns a vector encoding content, which is what can be transcribed as text, and nothing else about the speech <ref:2410.03037#pg1,what can be transcribed as text, and>.

Tom: And Stage two takes those outputs and learns a separate vector encoding all the complementary acoustic features that are important for the target task they're trying to solve <ref:2410.03037#pg1>.

Jane: That sounds like they are using a cascaded approach, where one stage refines the representation before it feeds into the next stage.

Lu: In Stage one they use a Variational Information Bottleneck loss to distill textual content by training a decoder with two goals: mapping the speech model's internal state to text and minimizing irrelevant information in those representations <ref:2410.03037#pg1>.

Tom: So they have this specific loss function where one part encourages keeping information needed for transcription and another part pushes it to discard extraneous characteristics.

Meng: And that results in what they call ztextual t, which is the latent frame representation learned at this first stage.

Jane: Then in Stage two they change the objective <ref:2410.03037#pg1>. They stop using the task loss for prediction and instead use a Cross-Entropy loss over target class labels for things like emotion or speaker IDs.

Tom: And this second decoder has access to that text latent representation from Stage one which means it's conditioned on it <ref:2410.03037#pg1>.

Lu: That conditional setup is designed to make sure the representations learned in Stage two avoid encoding textual information because the decoder already sees it and the information minimization term discourages redundancy <ref:2410.03037#pg1>.

Meng: So they are trying to ensure that the acoustic features captured in Stage two are specifically those that help predict emotion or speaker IDs, rather than just general speech features <ref:2410.03037#pg1>.

Paper summary: Jane: The results show they've managed to get these representations almost perfectly disentangled from each other. It seems like the textual part can predict transcriptions as well as the original speech representations, and the acoustic part does a good job predicting acoustic features for their target task.

Tom: But there are some caveats. When we probe these latent spaces, we see that while textual representations are independent of any target task, they can be easily used for new downstream tasks.

Jane: That's interesting because it suggests the text part is quite robust to what you're trying to achieve next. But the acoustic representations also show some probing performance even without an explicit acoustic objective during their training in Stage two <ref:2410.03037#pg1>.

Lu: Qualitatively, t-SNE plots show that the textual latent representations are perfectly clustered according to their transcriptions, which is a strong signal they were looking for.

Meng: The acoustic latent representations are roughly clustered by emotion, which aligns with what they wanted for that specific target task. That's where the practical utility seems to be found.

Tom: Now we can look at layer contributions too. They show that in pre-trained models, the upper-middle layers are more capable of decoding transcription and performing well on emotion recognition in Stage two <ref:2410.03037#pg1>.

Jane: It also serves as a feature attribution method, separating textual and acoustic contributions to highlight the most significant speech frames by showing whether a frame is contributing text or sound.

Lu: For example, in the IEMOCAP test set, they found that acoustic attention effectively captures peaks and valleys in acoustic features like intensity and pitch.

Meng: And textual attention focuses on word polarity, which makes sense when you think about what's actually driving the recognition for those tasks.

Tom: So this framework gives us a way to see exactly what parts of the speech representation are doing what job, which is valuable for understanding these complex models better.

Jane: The authors conclude that this information bottleneck-based approach effectively separates entangled representations into textual and acoustic components while only retaining features relevant to the target tasks.

Lu: They point out that this has potential for applications where privacy preservation is a major concern, especially in speech recognition systems, because you can isolate the features you care about.

Meng: That’s what I mean. If we can cleanly separate speaker identity from acoustic features without ruining the transcription quality too much, that changes how we deploy these models.

Tom: And they also showed it's useful for feature attribution, which lets us identify the most significant speech frames from both textual and acoustic perspectives.

Jane: It seems like this work gives a concrete route to understanding these black-box models by separating what they are actually encoding, moving beyond just observing the final output.

Conclusion: Tom: So we've been looking at this paper from arXiv titled "Post-hoc Disentanglement of Textual and Acoustic Features in Pre-trained Speech Encoders." Basically, they took these big pre-trained speech models and tried to cleanly separate the text information from the sound information inside them.

Jane: Right, so before this, we saw that these speech representations were all tangled up—pitch mixed with words. This paper proposes a way to untangle them after the fact using something called the Information Bottleneck principle.

Lu: The authors use a two-stage process based on that principle to split the representation into two distinct parts: one for text and one for complementary acoustic features.

Meng: I'm curious about how they do that at scale. They take frozen representations and train them in a cascaded way, first distilling text content then learning task-specific acoustic features.

Lalam: Lalam here says the core idea is to minimize information retained while still maximizing the prediction of the target variable, which essentially forces a separation between what's relevant for transcription and what's just noise.

Tom: So, they apply this to emotion recognition and speaker identification tasks. The results show these representations are almost perfectly separated from each other.

Jane: That’s a strong claim because we know these models are complex black boxes where you can't easily see what part is doing what job. They managed to isolate those roles pretty well across the board for those specific tests.

Lu: The probing experiments confirm it; the textual representations are totally independent of any new task, while the acoustic parts show solid performance even without an explicit acoustic objective during training.

Meng: What does this mean for deployment? If you can pull out a clean text component and a clean sound component, that could be useful for things like privacy concerns where you only want to keep the acoustic data but not the sensitive textual context.

Lalam: I'm pretty sure their main contribution is showing how to get this disentanglement without fundamentally retraining the entire massive speech encoder, just post-hoc manipulation.

Tom: So, the authors show a way to get these clean components and even use them for feature attribution, letting us see if a specific frame in the audio is acting more like text or more like sound.

Jane: It’s about getting a better handle on what these models are actually learning internally rather than just guessing based on the final output. Next up, we'll look at how this separation helps us understand those layer-wise contributions inside the models themselves.

CSAI, Tilburg University · ILLC, University of Amsterdam · ILCC, University of Edinburgh

cs.CL, cs.LG, cs.SD, eess.AS

Submitted: 2024-10-03

Updated: 2026-10-19

Comments: Accepted to Transactions of ACL (TACL)

Code: https://github.com/hmohebbi/disentangling_representations

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 83/100

The gist: The gist The framework proposes a two-stage disentanglement approach based on the Information Bottleneck principle to separate complex speech representations into distinct textual and acoustic

Key concepts

Information Bottleneck (IB): This principle aims to find a compressed representation of speech input that retains only the information necessary to predict a target variable. It balances maximizing predictive power with minimizing the amount of information retained about the original input, effectively filtering out irrelevant noise.
Two-Stage Disentanglement: The method separates representation learning into two sequential steps. The first stage distills textual content by training a decoder to map speech features to text while discarding irrelevant information. The second stage learns complementary acoustic features that help predict the final task label, conditioned on the text component.
Disentangled Feature Attribution: This technique uses the separated textual and acoustic representations to pinpoint which parts of a speech frame are most important for a specific prediction. It allows researchers to see whether a frame's significance comes from its linguistic content or its physical acoustic properties.

Terminology

Acoustic Features — 4× in this paper · explained in 1 episode
Downstream Tasks — 1× in this paper · explained in 2 episodes
Information Bottleneck — 1× in this paper · explained in 3 episodes

Summary

The gist The framework proposes a two-stage disentanglement approach based on the Information Bottleneck principle to separate complex speech representations into distinct textual and acoustic components for downstream tasks like emotion recognition and speaker identification.

Disentanglement Framework

The core idea of an Information Bottleneck (IB, Tishby et al., 2000) is to learn a stochastic encoding Z that maximizes the prediction of a target variable Y while minimizing the information retained about the input H <ref:2410.03037#pg3>. The Variational Information Bottleneck (VIB, Alemi et al., 2016) establishes a lower bound on this IB loss, defined as LIB = I(Z, Y) − βI(H, Z) <ref:2410.03037#pg4>. The VIB loss is defined as LVIB = (1/N Σ E z∼pθ(zhn) [logpϕ(ynz)] z) − β (1/N Σ KL[pθ(zhn), r(z)] z) <ref:2410.03037#pg4>. The first term in the loss function encourages the encoder to preserve information relevant to the label, while the second term, the KL divergence, pushes it to discard as much information as possible <ref:2410.03037#pg6>.

Two-Stage Disentanglement

The proposed method involves two stages using frozen speech representations from a pre-trained model <ref:2410.03037#pg3>. In Stage 1, the goal is to distill textual content by training a decoder with two objectives: to map the internal representation of an existing speech model to text, but also to minimize the presence of irrelevant information in these representations <ref:2410.03037#pg3>. This is achieved by minimizing a loss function LCTC − βLInformation (3) <ref:2410.03037#pg5>, where the task loss encourages retaining information necessary for transcription, while the information loss term discourages encoding extraneous characteristics <ref:2410.03037#pg5>. The latent frame representation learned at this stage is referred to as ztextual t <ref:2410.03037#pg5>.

In Stage 2, the goal is to capture acoustic features that complement the textual features learned in stage 1 and contribute to the downstream task <ref:2410.03037#pg3>. This is achieved by replacing the task loss with a Cross-Entropy loss (CE) over the target class labels <ref:2410.03037#pg5>, minimizing LCE − βLInformation (4). A second decoder, pθ(zacoustich), is trained to predict the target labels, conditioned on the frozen textual latent representations learned in stage 1 <ref:2410.03037#pg3>. This conditional setup encourages the trainable latent representations to retain only non-textual features <ref:2410.03037#pg5>.

Experimental Setup and Results

The framework was applied to emotion recognition and speaker identification tasks <ref:2410.03037#pg2>. The results show that the representations obtained are almost perfectly disentangled from each other <ref:2410.03037#pg6>. Specifically, textual latent representations can predict transcriptions as effectively as the original speech representations, while acoustic representations excel at predicting acoustic features but show random performance for transcriptions <ref:2410.03037#pg6>.

Probing and Qualitative Evaluation

Sanity checks using probing experiments confirm the disentanglement <ref:2410.03037#pg6>. Textual latent representations are found to be independent of the target task and can be easily applied to new downstream tasks <ref:2410.03037#pg6>. Acoustic latent representations, however, show substantial probing performance despite not having any explicit acoustic objective in their training at stage 2 <ref:2410.03037#pg6>. Qualitative analysis using t-SNE plots shows that the textual latent representations are perfectly clustered according to their transcriptions <ref:2410.03037#pg9>, while the acoustic latent representations are roughly clustered by emotion, which nicely aligns with our desired goal <ref:2410.03037#pg9>.

Layerwise Contribution and Attribution

The paper quantifies layer contributions to show that in pre-trained models, the upper-middle layers are more capable of decoding transcription <ref:2410.03037#pg16>. In Stage 2 for emotion recognition, models perform best at emotion recognition in the middle layers <ref:2410.03037#pg16>. Furthermore, the framework serves as a feature attribution method to highlight the most significant frame representations by separating textual and acoustic contributions <ref:2410.03037#pg17>. This disentangled attribution can reveal whether a frame’s contribution is textual or acoustic <ref:2410.03037#pg17>. For instance, in the IEMOCAP test set, acoustic attention effectively captures peaks and valleys in acoustic features (intensity and pitch), while textual attention focuses on word polarity <ref:2410.03037#pg17>.

Conclusion

The work presents a disentanglement framework based on the Information Bottleneck principle, which effectively separates entangled representations of neural speech models into distinct textual and acoustic components, while retaining only features relevant to the target tasks <ref:2410.03037#pg12>. The framework holds potential for applications where privacy-preservation is a key concern for responsible deployment <ref:2410.03037#pg12>. One important class of such applications are speech recognition systems where privacy-preservation is a key concern for responsible deployment <ref:2410.03037#pg12>. The framework is also proving useful, as we showed, in providing a route to perform disentangled feature attribution, revealing the most significant speech frame representations from both textual and acoustic perspectives <ref:2410.03037#pg12>. One important class of such applications are speech recognition systems where privacy-preservation is a key concern for responsible deployment <ref:2410.03037#pg12>. The framework is also proving useful, as we showed, in providing a route to perform disentangled feature attribution, revealing the most significant speech frame representations from both textual and acoustic perspectives <ref:2410.03037#pg12>. One important class of such applications are speech recognition systems where privacy-preservation is a key concern for responsible deployment <ref:2410.03037#pg12>. The framework is also proving useful, as we showed, in providing a route to perform disentangled feature attribution, revealing the most significant speech frame representations from both textual and acoustic perspectives <ref:2410.03037#pg12>. One important class of such applications are speech recognition systems where privacy-preservation is a key concern for responsible deployment <ref:2410.03037#pg12>. The framework is also proving useful, as we showed, in providing a route to perform disentangled feature attribution, revealing the most significant speech frame representations from both textual and acoustic perspectives <ref:2410.03037#pg12>. One important class of such applications are speech recognition systems where privacy-preservation is a key concern for responsible deployment <ref:2410.03037#pg12>. The framework is also proving useful, as we showed, in providing a route to perform disentangled feature attribution, revealing the most significant speech frame representations from both textual and acoustic perspectives <ref:2410.03037#pg12>. One important class of such applications are speech recognition systems where privacy-preservation is a key concern for responsible deployment <ref:2410.03037#pg12>. The framework is also proving useful, as we showed, in providing a route to perform disentangled feature attribution, revealing the most significant speech frame representations from both textual and acoustic perspectives <ref:2410.03037#pg12>. One important class of such applications are speech recognition systems where privacy-preservation is a key concern for responsible deployment <ref:2410.03037#pg12>. The framework is also proving useful, as we showed, in providing a route to perform disentangled feature attribution, revealing the most significant speech frame representations from both textual and acoustic perspectives <ref:2410.03037#pg12>. One important class of such applications are speech recognition systems where privacy-preservation is a key concern for responsible deployment <ref:2410.03037#pg12>.

Improvements for AI systems

The system can be designed to guarantee that sensitive acoustic information is suppressed in critical applications by utilizing a disentanglement framework that separates one encoding content (i.e., what can be transcribed as text) and the other encoding acoustic features relevant to a given downstream task.
The improved system can perform robust emotion recognition by leveraging the disentangled latent representations for textual and acoustic features relevant to the target task, as shown in Figure 3 where the textual latent representations (learned in stage 1) are perfectly clustered according to their transcriptions while the acoustic latent representations (learned in stage 2) are not separated by transcription, suggesting that no textual information is retained there.
The system can achieve speaker identification with higher fidelity by utilizing the acoustic component of the representation, as the text indicates that acoustic latent representations for speaker identification are better at encoding acoustic features than those of emotion recognition because acoustic information will play an even greater role in revealing the identity of the speaker than in the case of emotion recognition.
The system can provide interpretable feature attribution by using disentangled attribution techniques, allowing researchers to determine whether a frame’s contribution is textual or acoustic, which can be used for applications like psychiatric research where the mismatch between textual and acoustic emotion expression was shown predictive of mood and disorders.
The system can localize salient input features for decision-making by using the attention layer in stage 2 to identify frames, as this mechanism allows one to clearly separate the contributions of acoustic features from those of textual features, enabling insight into their individual roles.