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Found 9 papers, 3 papers with code
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 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.
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.
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
Acoustic Unit Discovery
on ZeroSpeech 2019 English
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.
