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Found 16 papers, 6 papers with code
LinearVC: Linear transformations of self-supervised features through the lens of voice conversion
We introduce LinearVC, a simple voice conversion method that sheds light on the structure of self-supervised representations.
Unsupervised Word Discovery: Boundary Detection with Clustering vs. Dynamic Programming
We look at the long-standing problem of segmenting unlabeled speech into word-like segments and clustering these into a lexicon.
Spoken-Term Discovery using Discrete Speech Units
Discovering a lexicon from unlabeled audio is a longstanding challenge for zero-resource speech processing.
Revisiting speech segmentation and lexicon learning with better features
We revisit a self-supervised method that segments unlabelled speech into word-like segments.
Rhythm Modeling for Voice Conversion
Voice conversion aims to transform source speech into a different target voice.
Voice Conversion With Just Nearest Neighbors
Any-to-any voice conversion aims to transform source speech into a target voice with just a few examples of the target speaker as a reference.
Ranked #1 on
Voice Conversion
on LibriSpeech test-clean
(using extra training data)
Visually grounded few-shot word acquisition with fewer shots
Our approach involves using the given word-image example pairs to mine new unsupervised word-image training pairs from large collections of unlabelled speech and images.
A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion
Specifically, we compare discrete and soft speech units as input features.
Analyzing Speaker Information in Self-Supervised Models to Improve Zero-Resource Speech Processing
In this paper, we first show that the per-utterance mean of CPC features captures speaker information to a large extent.
Towards localisation of keywords in speech using weak supervision
Of the two forms of supervision, the visually trained model performs worse than the BoW-trained model.
Towards unsupervised phone and word segmentation using self-supervised vector-quantized neural networks
We specifically constrain pretrained self-supervised vector-quantized (VQ) neural networks so that blocks of contiguous feature vectors are assigned to the same code, thereby giving a variable-rate segmentation of the speech into discrete units.
Vector-quantized neural networks for acoustic unit discovery in the ZeroSpeech 2020 challenge
The idea is to learn a representation of speech by predicting future acoustic units.
Ranked #1 on
Voice Conversion
on ZeroSpeech 2019 English
(using extra training data)
Online Constrained Model-based Reinforcement Learning
The environment's dynamics are learned from limited training data and can be reused in new task instances without retraining.
If dropout limits trainable depth, does critical initialisation still matter? A large-scale statistical analysis on ReLU networks
Our results therefore suggest that, in the shallow-to-moderate depth setting, critical initialisation provides zero performance gains when compared to off-critical initialisations and that searching for off-critical initialisations that might improve training speed or generalisation, is likely to be a fruitless endeavour.
Unsupervised acoustic unit discovery for speech synthesis using discrete latent-variable neural networks
For our submission to the ZeroSpeech 2019 challenge, we apply discrete latent-variable neural networks to unlabelled speech and use the discovered units for speech synthesis.
Will it Blend? Composing Value Functions in Reinforcement Learning
An important property for lifelong-learning agents is the ability to combine existing skills to solve unseen tasks.
