TS-SUPERB: Evaluating Target Speech Processing in Speech Self-Supervised Learning Models
The paper "TS-SUPERB: A Target Speech Processing Benchmark for Speech Self-Supervised Learning Models" introduces a novel benchmarking framework aimed at assessing speech self-supervised learning (SSL) models in scenarios involving target-speaker tasks. Traditional benchmarks for SSL models largely focus on single-speaker tasks, and this work addresses the need for evaluating models within noisy, multi-talker environments where identifying and processing a target speaker's speech is crucial.
Overview
The Target-Speaker Speech Processing Universal Performance Benchmark (TS-SUPERB) presents a comprehensive evaluation framework that integrates four key tasks pertinent to target-speaker processing:
- Target Speech Extraction (TSE): Involves isolating a target speaker's voice from a speech mixture.
- Personalized Speech Enhancement (PSE): Enhances the speech of a target speaker amidst background noise and other speakers.
- Target-Speaker Automatic Speech Recognition (TS-ASR): Accurately transcribes the speech of a designated speaker from a complex acoustic environment.
- Personal Voice Activity Detection (PVAD): Detects voice activity specifically linked to the target speaker within mixed audio inputs.
These tasks leverage the inherent strengths of SSL models by utilizing speaker embeddings derived from enroLLMent speech to condition downstream processing models. The approach accentuates the importance of evaluating SSL models under conditions where multiple speakers are present, proving that single-speaker task performance does not reliably indicate performance on target-speaker tasks.
Technical Contributions
- Unified Architecture: The benchmark adopts a unified target speech encoder architecture across all tasks, facilitating shared learning of speaker and feature extraction strategies. This architecture incorporates a speaker encoder and extractor module, fostering efficient joint optimization across related tasks.
- Comprehensive Evaluation: The research evaluates seven leading SSL models, providing a detailed comparison across TS-SUPERB tasks and traditional single-speaker tasks like ASR, SV, and speech separation (Sep). Notably, WavLM models showed superior performance in target speaker scenarios, attributed to their training data augmentation strategies.
- Multi-Task Learning: By exploring joint optimization for TSE and TS-ASR tasks, as well as PSE and PVAD tasks, the research reveals potential performance improvements through multi-task learning. The findings imply that shared architecture and parameter exploration can enhance SSL models' applicability in target-speaker scenarios.
- Layer-Wise Analysis: Through layer-wise probing, the paper delineates the significance of different SSL model layers in extracting the relevant features for target-speaker tasks. Lower layers contribute to speaker-context extraction, while higher layers encode linguistic information, especially crucial for tasks requiring semantic understanding like TS-ASR.
Results and Implications
The results underscore the growing capability of self-supervised models in handling complex multi-speaker environments efficiently. However, TS-SUPERB reveals the current limitations and challenges in achieving top-tier results compared to dedicated systems like TD-SpeakerBeam. These findings open avenues for further exploration into improving SSL model architectures and training processes to better handle multi-talker scenarios.
Future Directions
This work paves the way for more sophisticated SSL models capable of excelling in diverse and challenging environments. Advancements could focus on fine-tuning strategies, model architectures like Conformer, and cross-task parameter sharing to enhance SSL models' performance further. The exploration and development of multitasking models trained on increased data variability and condition complexity could significantly improve the robustness and applicability of SSL models in real-world applications.
In summary, TS-SUPERB represents a significant step toward comprehensive evaluation frameworks tailored for target speech processing, emphasizing the intricate challenges posed by multi-speaker environments and heralding potential improvements in SSL capabilities.