Findings of the 2023 ml-superb challenge: Pre-training and evaluation over more languages and beyond
The 2023 Multilingual Speech Universal Performance Benchmark (ML-SUPERB) Challenge
expands upon the acclaimed SUPERB framework, emphasizing self-supervised models in …
expands upon the acclaimed SUPERB framework, emphasizing self-supervised models in …
ML-SUPERB 2.0: Benchmarking Multilingual Speech Models Across Modeling Constraints, Languages, and Datasets
ML-SUPERB evaluates self-supervised learning (SSL) models on the tasks of language
identification and automatic speech recognition (ASR). This benchmark treats the models as …
identification and automatic speech recognition (ASR). This benchmark treats the models as …
DQ-Data2vec: Decoupling Quantization for Multilingual Speech Recognition
Data2vec is a self-supervised learning (SSL) approach that employs a teacher-student
architecture for contextual representation learning via masked prediction, demonstrating …
architecture for contextual representation learning via masked prediction, demonstrating …