Improving Noise Robustness of Synthetic Speech Detection through Dual-Branch Knowledge Distillation

Core Concepts

A dual-branch knowledge distillation method is proposed to improve the noise robustness of synthetic speech detection, utilizing interactive fusion and response-based teacher-student paradigms.

Abstract

The paper proposes a dual-branch knowledge distillation method for noise-robust synthetic speech detection (DKDSSD). The key aspects are:

Speech enhancement is applied at the front-end of the student branch to reduce noise interference. However, speech enhancement can introduce speech distortion, so an interactive fusion module is proposed to adaptively combine the denoised features and the original noisy features to mitigate the impact of speech distortion.

Knowledge distillation is used to guide the training of the noisy student branch. A response-based teacher-student paradigm is proposed, which maps the student's decision space to the teacher's decision space, enabling the noisy speech to behave similarly to clean speech.

Joint training is employed to optimize the entire structure, including the speech enhancement module and the synthetic speech detection module, to achieve global optimality.

Extensive experiments on multiple simulated noisy datasets and official datasets show that the proposed DKDSSD method outperforms the cascade system or joint training method, while maintaining performance on clean scenes. In cross-dataset experiments, the proposed method also exhibits the best generalization.

Stats

Additive noise can significantly degrade the performance of synthetic speech detectors trained on pure speech.
Noisy speech with SNR randomly sampled between 0-20dB is used for training, and 5 fixed SNR levels (0, 5, 10, 15, 20dB) are used for testing.
The proposed DKDSSD method achieves the lowest EER of 3.55% and 5.40% on the seen and unseen noisy datasets of ASVspoof 2019 LA, respectively.

Quotes

"To improve the performance in noisy scenes, this paper proposes a dual-branch knowledge distillation method for noise-robust synthetic speech detection (DKDSSD)."
"Knowledge distillation promotes students to learn the classification ability of clean teachers. It involves mapping the decision space of the student model to that of the teacher."
"Interactive fusion is proposed to enable the channel interaction of denoised and noisy features, and fuse them at the spatial level, adaptively reducing noise interference and balancing distortion issues."

Key Insights Distilled From

Dual-Branch Knowledge Distillation for Noise-Robust Synthetic Speech Detection

by Cunhang Fan,... at arxiv.org 04-17-2024

https://arxiv.org/pdf/2310.08869.pdf

Deeper Inquiries

How could the proposed DKDSSD method be extended to handle more diverse noise types, such as non-stationary and colored noise?

The proposed DKDSSD method can be extended to handle more diverse noise types by incorporating additional modules or techniques specifically designed to address non-stationary and colored noise. Here are some ways to enhance the DKDSSD method for handling diverse noise types:

Adaptive Feature Extraction: Introduce adaptive feature extraction techniques that can dynamically adjust to different noise characteristics. This can involve incorporating time-frequency analysis methods that are robust to non-stationary noise patterns.

Dynamic Noise Modeling: Develop a dynamic noise modeling module that can adapt to different noise profiles. This module can continuously analyze the incoming noise signals and adjust its parameters to effectively denoise the speech signals.

Multi-Task Learning: Implement multi-task learning strategies where the model is trained on a variety of noise types simultaneously. By exposing the model to a diverse range of noise during training, it can learn to generalize better to unseen noise types.

Data Augmentation: Increase the diversity of the training data by augmenting the dataset with a wide range of noise types, including non-stationary and colored noise. This will help the model learn robust features that are resilient to various noise conditions.

Transfer Learning: Utilize transfer learning techniques to fine-tune the model on specific noise types. By pre-training the model on a broad range of noise types and then fine-tuning it on the target noise type, the model can adapt more effectively to diverse noise profiles.

By incorporating these strategies, the DKDSSD method can be extended to handle a broader spectrum of noise types, including non-stationary and colored noise, improving its robustness and generalization capabilities.

How could the proposed techniques be applied to other speech-related tasks beyond synthetic speech detection, such as speech recognition or speaker verification?

The techniques proposed in the DKDSSD method can be adapted and applied to other speech-related tasks beyond synthetic speech detection, such as speech recognition or speaker verification. Here's how these techniques can be leveraged for other tasks:

Speech Recognition:

Feature Fusion: The interactive fusion module can be utilized to combine denoised and noisy features for improved speech recognition in noisy environments.
Knowledge Distillation: The response-based knowledge distillation approach can be used to transfer knowledge from a teacher model to a student model in speech recognition tasks, enhancing model performance.

Speaker Verification:

Speech Enhancement: The speech enhancement module can be employed to preprocess speech signals before speaker verification, reducing the impact of noise and improving verification accuracy.
Joint Training: Joint training of speech enhancement and speaker verification models can enhance the robustness of the system to noisy conditions.

Multi-Task Learning:

Adaptation to Different Tasks: The multi-task learning approach used in DKDSSD can be extended to simultaneously train models for multiple speech-related tasks, such as speech recognition, speaker verification, and synthetic speech detection.

Transfer Learning:

Domain Adaptation: Transfer learning techniques can be applied to adapt pre-trained models from synthetic speech detection to other speech tasks, fine-tuning them on specific datasets for improved performance.

By applying the proposed techniques to these speech-related tasks, it is possible to enhance their robustness, accuracy, and generalization capabilities in noisy environments.