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Found 9 papers, 4 papers with code
Generative Spoken Dialogue Language Modeling
We introduce dGSLM, the first "textless" model able to generate audio samples of naturalistic spoken dialogues.
textless-lib: a Library for Textless Spoken Language Processing
Textless spoken language processing research aims to extend the applicability of standard NLP toolset onto spoken language and languages with few or no textual resources.
Textless Speech Emotion Conversion using Decomposed & Discrete Representations
We decompose speech into discrete and disentangled learned representations, consisting of content units, F0, speaker, and emotion.
Textless Speech Emotion Conversion using Discrete and Decomposed Representations
We use a decomposition of the speech signal into discrete learned representations, consisting of phonetic-content units, prosodic features, speaker, and emotion.
ASR4REAL: An extended benchmark for speech models
Popular ASR benchmarks such as Librispeech and Switchboard are limited in the diversity of settings and speakers they represent.
Text-Free Prosody-Aware Generative Spoken Language Modeling
Generative Spoken Language Modeling (GSLM) \cite{Lakhotia2021} is the only prior work addressing the generative aspects of speech pre-training, which replaces text with discovered phone-like units for language modeling and shows the ability to generate meaningful novel sentences.
Speech Resynthesis from Discrete Disentangled Self-Supervised Representations
We propose using self-supervised discrete representations for the task of speech resynthesis.
Generative Spoken Language Modeling from Raw Audio
We introduce Generative Spoken Language Modeling, the task of learning the acoustic and linguistic characteristics of a language from raw audio (no text, no labels), and a set of metrics to automatically evaluate the learned representations at acoustic and linguistic levels for both encoding and generation.
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Resynthesis
on LJSpeech
Radarly : \'ecouter et analyser le web conversationnel en temps r\'eel (Real time listening and analysis of the social web using Radarly)
De par le contexte conversationnel digital, l{'}outil Radarly a {\'e}t{\'e} con{\c{c}}u pour permettre de traiter de grands volumes de donn{\'e}es h{\'e}t{\'e}rog{\`e}nes en temps r{\'e}el, de g{\'e}n{\'e}rer de nouveaux indicateurs et de les visualiser sur une interface coh{\'e}rente et confortable afin d{'}en tirer des analyses et {\'e}tudes pertinentes.
