SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Generative Capabilities (2203.06849v1)

Abstract: Transfer learning has proven to be crucial in advancing the state of speech and natural language processing research in recent years. In speech, a model pre-trained by self-supervised learning transfers remarkably well on multiple tasks. However, the lack of a consistent evaluation methodology is limiting towards a holistic understanding of the efficacy of such models. SUPERB was a step towards introducing a common benchmark to evaluate pre-trained models across various speech tasks. In this paper, we introduce SUPERB-SG, a new benchmark focused on evaluating the semantic and generative capabilities of pre-trained models by increasing task diversity and difficulty over SUPERB. We use a lightweight methodology to test the robustness of representations learned by pre-trained models under shifts in data domain and quality across different types of tasks. It entails freezing pre-trained model parameters, only using simple task-specific trainable heads. The goal is to be inclusive of all researchers, and encourage efficient use of computational resources. We also show that the task diversity of SUPERB-SG coupled with limited task supervision is an effective recipe for evaluating the generalizability of model representation.

• The paper presents an enhanced benchmark that integrates semantic and generative tasks to rigorously evaluate speech models.
• It uses a frozen pre-trained model approach with lightweight, task-specific heads, reducing computational demands while supporting diverse model comparisons.
• Findings show that no single model excels consistently, underscoring the benchmark’s role in guiding future speech processing research.

Overview of SUPERB-SG: Enhanced Speech Processing Benchmark

The paper "SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Generative Capabilities" addresses the limitations and challenges in evaluating pre-trained models in speech processing. The researchers propose SUPERB-SG, an expanded benchmark that enhances the Speech processing Universal PERformance Benchmark (SUPERB) by incorporating tasks that assess semantic and generative capabilities.

Methodology and Objectives

SUPERB-SG introduces a principled evaluation framework for pre-trained models by integrating more diverse and challenging tasks than its predecessor. The benchmark focuses on semantic understanding and generative aspects that are crucial for advancing speech interfaces. By freezing the parameters of pre-trained models, the evaluation relies on lightweight, task-specific trainable heads, which reduces computational demands and encourages inclusivity among researchers with varying resources.

The primary tasks in SUPERB-SG include:

• Speech Translation (ST): Evaluates the model's ability to translate spoken language into text, requiring a deep understanding of linguistic and semantic content.
• Out-of-domain ASR (OOD-ASR): Focuses on evaluating cross-lingual and spontaneous speech recognition, testing the adaptability of models to unseen domains.
• Voice Conversion (VC): Assesses the model’s capacity to transfer voice characteristics from any speaker to a target speaker while maintaining linguistic content.
• Speech Separation (SS) and Enhancement (SE): Tests the ability to separate and enhance speech from noisy environments, examining the generative capabilities of models.

Results and Analysis

The evaluation spans 15 self-supervised learning (SSL) models, with a detailed analysis provided on their performance across these tasks. Notably, no single model consistently outperforms others; however, models like HuBERT Large exhibit competitive performance on semantic and generative tasks. This suggests that while SSL models have advanced capabilities in linguistic representation, their consistency varies based on task requirements.

A detailed correlation analysis among the tasks reveals substantial alignment in tasks requiring similar knowledge domains, with content recognition tasks like ASR and ST showing stronger correlations. This supports the reliability of the benchmark in assessing shared representation skills across tasks.

Implications and Future Directions

The introduction of SUPERB-SG serves to democratize speech research by establishing a comprehensive framework for evaluating pre-trained models. The benchmark’s expanded task set highlights the potential for future research in creating generalized, efficient models that excel in diverse speech processing domains.

The methodology emphasizes robustness through variations in downstream model architectures and training data sizes, demonstrating the framework’s validity and applicability in real-world settings. By encouraging advancements in self-supervised learning methods and facilitating standardized evaluations, SUPERB-SG aims to guide the development of more powerful, generalizable models for the speech research community.

Conclusion

SUPERB-SG represents a significant step towards a unified, rigorous evaluation of SSL models in speech processing. By incorporating diverse semantic and generative tasks, it provides a versatile benchmark for the continued evolution of speech technologies in both research and practical applications. Researchers are encouraged to leverage the open-sourced code and participate in the ongoing challenge to drive further innovation in this field.