Learning Contextualized Representation On Discrete Space Via Hierarchical Product Quantization
Abstract
Self-supervised learning has recently demonstrated significant success in various speech processing applications. Recent studies report that pre-training with contextualized continuous targets plays a crucial role in fine-tuning for better speech downstream tasks. However, unlike the continuous targets, it is challenging to produce contextualized targets on discrete space due to unstable training.
- ConferenceICASSP
- TopicSpeech Recognition
- Year Published2024
Abstract
Self-supervised learning has recently demonstrated significant success in various speech processing applications. Recent studies report that pre-training with contextualized continuous targets plays a crucial role in fine-tuning for better speech downstream tasks. However, unlike the continuous targets, it is challenging to produce contextualized targets on discrete space due to unstable training.