Disentangling Textual and Acoustic Features of Neural Speech Representations

Abstract: Neural speech models build deeply entangled internal representations, which capture a variety of features (e.g., fundamental frequency, loudness, syntactic category, or semantic content of a word) in a distributed encoding. This complexity makes it difficult to track the extent to which such representations rely on textual and acoustic information, or to suppress the encoding of acoustic features that may pose privacy risks (e.g., gender or speaker identity) in critical, real-world applications. In this paper, we build upon the Information Bottleneck principle to propose a disentanglement framework that separates complex speech representations into two distinct components: one encoding content (i.e., what can be transcribed as text) and the other encoding acoustic features relevant to a given downstream task. We apply and evaluate our framework to emotion recognition and speaker identification downstream tasks, quantifying the contribution of textual and acoustic features at each model layer. Additionally, we explore the application of our disentanglement framework as an attribution method to identify the most salient speech frame representations from both the textual and acoustic perspectives.

• The paper proposes a novel two-stage VIB-based framework that successfully disentangles textual content from acoustic features in neural speech models.
• It demonstrates that the separated representations preserve transcription accuracy and capture key acoustic details for tasks like emotion and speaker identification.
• The method offers practical benefits by improving interpretability and addressing privacy concerns through selective filtering of sensitive acoustic information.

Disentangling Textual and Acoustic Features of Neural Speech Representations

The paper "Disentangling Textual and Acoustic Features of Neural Speech Representations" presents a framework for separating complex neural speech model representations into distinct textual and acoustic components. The primary focus lies in addressing the challenges posed by the entangled nature of current neural speech systems, such as Wav2Vec2 and HuBERT. These models, while effective, encode multiple speech features simultaneously, complicating interpretability and privacy concerns.

Framework and Methodology

The researchers propose a disentanglement framework based on the Information Bottleneck (IB) principle, employing Variational Information Bottleneck (VIB) for tractability. The framework consists of two stages designed to effectively separate textual content from acoustic features.

Stage 1: This phase targets retaining only the textual content necessary for transcription. Using a decoder guided by a Connectionist Temporal Classification (CTC) loss, the model learns to map internal speech representations into a condensed form, which preserves transcription-relevant details while filtering out other characteristics.

Stage 2: Focused on capturing acoustic information pertinent to specific tasks (e.g., emotion recognition or speaker identification), this stage uses the previously trained decoder and latent textual representations. The aim here is to encode additional acoustic features relevant to the downstream task while minimizing redundant information.

Results and Evaluation

The framework was evaluated on two downstream tasks: emotion recognition using the IEMOCAP dataset and speaker identification with the Common Voice dataset. Employing different sizes of Wav2Vec2 and HuBERT models, both pre-trained and fine-tuned, the results demonstrate that the disentangled representations achieve strong performance comparable to the original representations. Notably, the latent acoustic representations were able to encode key acoustic features, while the latent textual representations maintained high transcription accuracy.

The probing experiments conducted further validate the effectiveness of disentanglement. Textual latent representations showed little ability to predict acoustic features, while acoustic representations were unable to effectively predict text content, thus confirming successful disentanglement.

Implications and Future Directions

The separation of textual and acoustic features in speech models has significant implications both practically and theoretically. Practically, it presents potential solutions for privacy concerns by allowing acoustic features, such as speaker identity, to be selectively suppressed in specific applications. Theoretically, it enhances the understanding of neural speech representations, offering insights into the distribution of acoustic and textual information across model layers.

Future work may extend this framework to other modalities or investigate its application in more nuanced tasks, such as detecting bias in speech agents. Additionally, exploring the disentanglement of further types of acoustic features, like prosody or emotional tone, could provide additional utility in fields such as mental health diagnostics or human-computer interaction.

Conclusion

This research presents a robust framework for disentangling textual and acoustic features within neural speech models, maintaining task performance and enhancing model interpretability. By addressing the entangled nature of current neural representations, it contributes to the broader goal of developing more transparent and robust AI systems.