Exploration of Efficient End-to-End ASR using Discretized Input from Self-Supervised Learning

Understand

Self-supervised learning (SSL) of speech has shown impressive results in speech-related tasks, particularly in automatic speech recognition (ASR).

• While most methods employ the output of intermediate layers of the SSL model as real-valued features for downstream tasks, there is potential in exploring alternative approaches that use discretized token sequences.
• This approach offers benefits such as lower storage requirements and the ability to apply techniques from natural language processing.
• In this paper, we propose a new protocol that utilizes discretized token sequences in ASR tasks, which includes de-duplication and sub-word modeling to enhance the input sequence.