Noise-Robust Target-Speaker Voice Activity Detection Through Self-Supervised Pretraining

Abstract: Target-Speaker Voice Activity Detection (TS-VAD) is the task of detecting the presence of speech from a known target-speaker in an audio frame. Recently, deep neural network-based models have shown good performance in this task. However, training these models requires extensive labelled data, which is costly and time-consuming to obtain, particularly if generalization to unseen environments is crucial. To mitigate this, we propose a causal, Self-Supervised Learning (SSL) pretraining framework, called Denoising Autoregressive Predictive Coding (DN-APC), to enhance TS-VAD performance in noisy conditions. We also explore various speaker conditioning methods and evaluate their performance under different noisy conditions. Our experiments show that DN-APC improves performance in noisy conditions, with a general improvement of approx. 2% in both seen and unseen noise. Additionally, we find that FiLM conditioning provides the best overall performance. Representation analysis via tSNE plots reveals robust initial representations of speech and non-speech from pretraining. This underscores the effectiveness of SSL pretraining in improving the robustness and performance of TS-VAD models in noisy environments.

• The paper introduces DN-APC, a causal self-supervised pretraining framework that enhances TS-VAD performance in challenging noisy environments.
• It leverages noise-augmented, autoregressive prediction techniques to learn robust feature representations for real-time voice activity detection.
• Experimental results show an average performance gain of ~2%, with FiLM conditioning yielding superior integration of target-speaker characteristics.

Noise-Robust Target-Speaker Voice Activity Detection: An Analytical Overview

The paper "Noise-Robust Target-Speaker Voice Activity Detection Through Self-Supervised Pretraining" provides a compelling study on improving Target-Speaker Voice Activity Detection (TS-VAD) systems in challenging acoustic conditions using a self-supervised learning (SSL) framework. The authors propose a causal SSL pretraining approach named Denoising Autoregressive Predictive Coding (DN-APC), aiming to enhance TS-VAD's performance, particularly in noisy environments.

Core Contributions and Methodology

The authors address the inherent difficulty of training TS-VAD models due to the extensive necessity for labeled data, which is usually resource-intensive to collect. To alleviate this requirement, they incorporate a self-supervised pretraining phase. The DN-APC framework enables the model to learn robust feature representations without the need for these extensive labels. The primary benefits of this model include:

1. Causal Self-Supervised Learning: This approach focuses on preparing the model to be robust in real-time applications. The DN-APC model is trained to predict future signal frames based on past observations, which inherently makes it causal and suitable for streaming applications.
2. Noise-Robustness Enhancement: The DN-APC framework incorporates denoising principles by augmenting the training data with noise and reverberation, which helps the model become more robust to distortions encountered in practical scenarios.
3. Evaluation of Speaker Conditioning Methods: The paper examines several conditioning strategies to incorporate target-speaker information, such as concatenation, addition, multiplication, and Featurewise Linear Modulation (FiLM). Each method attempts to seamlessly integrate speaker-specific characteristics into the voice activity detection task to improve accuracy.

Results and Observations

Experimental evaluations presented indicate that the DN-APC framework consistently improves the TS-VAD across various noisy conditions. In particular, models pretrained with DN-APC achieved an average performance improvement of approximately 2% in both seen and unseen noise environments. Specific observations include:

• Performance Improvement: The DN-APC pretrained models show marked performance gains across all noise levels, from clean to -5dB SNR, indicating enhanced robustness and generalization.
• Effect of Speaker Conditioning: Among the different conditioning methods explored, FiLM demonstrated superior performance overall, although multiplication was particularly effective for distinguishing target speech.
• Representation Learning Analysis: An analysis of learned representations showed that the pretrained models possessed a stronger capacity to separate speech from non-speech inputs, highlighting the efficacy of DN-APC in providing robust initial representations before fine-tuning for specific tasks.

Implications and Future Directions

The findings of this study have considerable implications for the development of TS-VAD systems that can operate reliably in dynamic and noisy environments, such as those encountered by voice-activated assistants and hearing aids. The causal SSL approach could also be extended and adapted for other streaming audio analysis tasks, thereby enhancing real-time audio processing applications.

Future research could focus on expanding SSL techniques further by exploring more advanced pretext tasks and augmentations, possibly incorporating multimodal data to improve the robustness of systems in highly varied conditions. Additionally, exploring the computational efficiency of these models for deployment in low-power devices could widen practical application scenarios.

In conclusion, this work provides valuable insights into the use of self-supervised learning to enhance TS-VAD systems' robustness and offers a promising direction for future improvements in auditory detection technologies.