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Speech Tokenization

Speech tokenization is the task of representing speech signals as a sequence of discrete units. Such representations can be later used for various downstream tasks including automatic speech recognition, text-to-speech, etc. Such representation serves as the basis of Speech Language Models.

Papers

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Showing 11–20 of 21 papers

TitleStatusHype
Scaling Properties of Speech Language Models—0
STAB: Speech Tokenizer Assessment Benchmark—0
UniWav: Towards Unified Pre-training for Speech Representation Learning and Generation—0
BASE TTS: Lessons from building a billion-parameter Text-to-Speech model on 100K hours of data—0
DC-Spin: A Speaker-invariant Speech Tokenizer for Spoken Language Models—0
Discrete Multimodal Transformers with a Pretrained Large Language Model for Mixed-Supervision Speech Processing—0
Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models—0
Factorized RVQ-GAN For Disentangled Speech Tokenization—0
Impact of Frame Rates on Speech Tokenizer: A Case Study on Mandarin and English—0
LAST: Language Model Aware Speech Tokenization—0
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