-
Towards unsupervised phone and word segmentation using self-supervised vector-quantized neural networks
We investigate segmenting and clustering speech into low-bitrate phone-l...
12/14/2020 ∙ by Herman Kamper, et al. ∙
0
∙
share
read it
-
Towards localisation of keywords in speech using weak supervision
Developments in weakly supervised and self-supervised models could enabl...
12/14/2020 ∙ by Kayode Olaleye, et al. ∙
0
∙
share
read it
-
Vector-quantized neural networks for acoustic unit discovery in the ZeroSpeech 2020 challenge
In this paper, we explore vector quantization for acoustic unit discover...
05/19/2020 ∙ by Benjamin van Niekerk, et al. ∙
0
∙
share
read it
-
Online Constrained Model-based Reinforcement Learning
Applying reinforcement learning to robotic systems poses a number of cha...
04/07/2020 ∙ by Benjamin van Niekerk, et al. ∙
6
∙
share
read it
-
If dropout limits trainable depth, does critical initialisation still matter? A large-scale statistical analysis on ReLU networks
Recent work in signal propagation theory has shown that dropout limits t...
10/13/2019 ∙ by Arnu Pretorius, et al. ∙
0
∙
share
read it
-
Unsupervised acoustic unit discovery for speech synthesis using discrete latent-variable neural networks
For our submission to the ZeroSpeech 2019 challenge, we apply discrete l...
04/16/2019 ∙ by Ryan Eloff, et al. ∙
0
∙
share
read it
-
Will it Blend? Composing Value Functions in Reinforcement Learning
An important property for lifelong-learning agents is the ability to com...
07/12/2018 ∙ by Benjamin van Niekerk, et al. ∙
0
∙
share
read it
