Disentangling Speaker Information from Speech Representation using Variable-Length Soft Pooling

Core Concepts

The core message of this paper is to remove speaker information from speech representations by exploiting the structured nature of speech and using variable-length soft pooling based on predicted boundaries.

Abstract

The paper proposes a self-supervised speech representation learning framework that aims to disentangle speaker information from speech representations. The key ideas are:

Exploit the structured nature of speech composed of discrete linguistic units with clear boundaries.
Use a neural network to predict these boundaries, enabling variable-length pooling for event-based representation extraction instead of fixed-rate methods.
The boundary predictor outputs a probability between 0 and 1, making the pooling "soft".
The model is trained to minimize the difference between the pooled representation of the original data and the data augmented by time-stretch and pitch-shift, encouraging the learned representation to be independent of speaker information.
The model is evaluated on the libri-light phonetic ABX task and the SUPERB speaker identification task, showing that the learned representation contains phonetic information but is independent of speaker information.
The paper also shows that the predicted boundaries align well with phoneme boundaries, even without explicit supervision.

Stats

The average number of phonemes per second is about 10, and the frame rate of the feature extractor is 100.
The training took about 60 hours for 600,000 steps using 2 RTX 3080ti GPUs with a batch size of 32.

Quotes

"To disentangle non-linguistic information, we will employ a prior assumption that speech is constructed from discrete linguistic units, which are discernible through distinct boundaries."
"Our objective is to minimize the presence of speaker-related information by augmenting the data through temporal misalignment and subsequently tasking the model with predicting the altered boundaries."

Key Insights Distilled From

Removing Speaker Information from Speech Representation using Variable-Length Soft Pooling

by Injune Hwang... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00856.pdf

Deeper Inquiries

How can the proposed framework be extended to other modalities beyond speech, such as text or images, to disentangle different types of information?

The proposed framework's concept of soft pooling and boundary prediction can be extended to other modalities by adapting the model architecture and input data. For text data, the boundaries could represent sentence or paragraph breaks, allowing the model to focus on capturing semantic content while minimizing author-specific information. In the case of images, boundaries could correspond to object boundaries or regions of interest, enabling the model to extract content-related features while reducing background or context-specific details. By training the model on diverse datasets from various modalities and adjusting the boundary prediction mechanism accordingly, it can effectively disentangle different types of information across multiple domains.

What are the potential limitations of the soft pooling approach, and how could it be further improved to better capture the hierarchical structure of speech?

One potential limitation of the soft pooling approach is the reliance on predicted boundaries, which may not always align perfectly with the hierarchical structure of speech, leading to information loss or misalignment. To address this limitation and improve the model's ability to capture the hierarchical structure of speech more effectively, several enhancements can be considered.

Fine-tuning Boundary Prediction: Refining the boundary prediction mechanism through additional training or fine-tuning can help align the predicted boundaries more accurately with the actual phoneme boundaries, enhancing the model's ability to capture the hierarchical structure.
Incorporating Contextual Information: Introducing contextual information or memory mechanisms within the soft pooling module can help the model better understand the sequential dependencies in speech data, allowing for more precise boundary predictions and improved representation extraction.
Hierarchical Attention Mechanisms: Implementing hierarchical attention mechanisms that consider multiple levels of granularity in the speech data can enable the model to capture the hierarchical structure more effectively, focusing on both local and global dependencies within the data.

Given the alignment between the predicted boundaries and phoneme boundaries, how could this information be leveraged to improve downstream tasks like speech recognition or synthesis?

The alignment between predicted boundaries and phoneme boundaries offers valuable insights that can be leveraged to enhance downstream tasks like speech recognition or synthesis in the following ways:

Improved Segmentation: By utilizing the predicted boundaries as guidance, speech recognition systems can benefit from more accurate phoneme segmentation, leading to enhanced transcription accuracy and better understanding of spoken language.
Enhanced Feature Extraction: Leveraging the aligned boundaries can help in extracting more informative features for speech synthesis tasks, enabling the generation of more natural and contextually relevant speech output.
Speaker Disentanglement: The alignment between boundaries can aid in disentangling speaker-related information from speech representations, contributing to better speaker-independent models for both recognition and synthesis tasks.
Adaptive Model Behavior: Models can adapt their behavior based on the predicted boundaries, focusing on specific phoneme sequences or linguistic units during recognition or synthesis, leading to more contextually appropriate and coherent speech output.