SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Generative Capabilities

May 22, 2022

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.

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AUTHORS

Written by

Annie Dong

Abdelrahman Mohamed

Shang-Wen Li

Andy T. Liu

Harry Chang

Hung-yi Lee

Jeff Lai

Jiatong Shi

Kushal Lakhotia

Phil Hall

Ray Chen

Sean Tsai

Shinji Watanabe

Shu-Wen Yang

Wenchin Huang

Xuankai Chang

Zili Huang

Publisher

ACL