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NAST: Noise Aware Speech Tokenization for Speech Language Models
Shoval Messica
,
Yossi Adi
The Rachel and Selim Benin School of Engineering and Computer Science
Research output
:
Contribution to journal
›
Conference article
›
peer-review
6
Scopus citations
Overview
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Dive into the research topics of 'NAST: Noise Aware Speech Tokenization for Speech Language Models'. Together they form a unique fingerprint.
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Keyphrases
Language Model
100%
Tokenization
100%
Speech-language
100%
Noise-aware
100%
Speech Signal
25%
Decoder
25%
Encoder
25%
Main Components
25%
Signal Variation
25%
Reverberation
25%
Training Model
25%
Disentanglement
25%
Automatic Speech Recognition
25%
Discrete Units
25%
Modelling Task
25%
Downstream Task
25%
Spoken Language Modeling
25%
Time Stretching
25%
Text-to-speech
25%
Pitch Shifting
25%
Computer Science
Language Modeling
100%
Lexical Tokenization
100%
Spoken Language
25%
Main Component
25%
Speech Recognition
25%
Text To Speech
25%
Pre-Trained Model
25%