Speech Benchmark

Speech benchmark research aims to create standardized evaluations for various speech processing tasks, enabling objective comparisons of different models and algorithms. Current efforts focus on developing comprehensive benchmarks encompassing diverse tasks (speech recognition, speaker identification, emotion recognition, etc.), exploring effective discrete audio representations (e.g., semantic tokens), and addressing challenges like low-resource scenarios and cross-lingual adaptability, often employing transformer-based architectures and self-supervised learning. These advancements are crucial for improving the robustness and generalizability of speech technologies, impacting applications ranging from clinical healthcare to personalized assistive devices.