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Found 9 papers, 6 papers with code
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.
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.
The Zero Resource Speech Challenge 2021: Spoken language modelling
We present the Zero Resource Speech Challenge 2021, which asks participants to learn a language model directly from audio, without any text or labels.
VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation
We introduce VoxPopuli, a large-scale multilingual corpus providing 100K hours of unlabelled speech data in 23 languages.
Ranked #1 on
Speech Recognition
on Common Voice French
(using extra training data)
The Zero Resource Speech Benchmark 2021: Metrics and baselines for unsupervised spoken language modeling
We introduce a new unsupervised task, spoken language modeling: the learning of linguistic representations from raw audio signals without any labels, along with the Zero Resource Speech Benchmark 2021: a suite of 4 black-box, zero-shot metrics probing for the quality of the learned models at 4 linguistic levels: phonetics, lexicon, syntax and semantics.
Data Augmenting Contrastive Learning of Speech Representations in the Time Domain
Contrastive Predictive Coding (CPC), based on predicting future segments of speech based on past segments is emerging as a powerful algorithm for representation learning of speech signal.
Unsupervised pretraining transfers well across languages
Cross-lingual and multi-lingual training of Automatic Speech Recognition (ASR) has been extensively investigated in the supervised setting.
Libri-Light: A Benchmark for ASR with Limited or No Supervision
Additionally, we provide baseline systems and evaluation metrics working under three settings: (1) the zero resource/unsupervised setting (ABX), (2) the semi-supervised setting (PER, CER) and (3) the distant supervision setting (WER).
Ranked #1 on
Speech Recognition
on Libri-Light test-other
(ABX-across metric)
