Universal Performance Benchmark

Universal performance benchmarks in speech processing aim to provide standardized evaluations of self-supervised learning (SSL) models across diverse tasks, fostering fair comparisons and accelerating research progress. Current efforts focus on expanding benchmark scope to include multilingual capabilities, various audio modalities (including audio-visual), and generative tasks alongside traditional discriminative ones, often employing transformer-based architectures. These benchmarks are crucial for identifying robust and generalizable speech representations, ultimately improving applications like speech recognition, speaker identification, and multimodal language models.