17 citations · 60 across the 28 of their papers we have counts for
8 papers · 1 filter
Multi-scale Octave Convolutions for Robust Speech Recognition
Joanna Rownicka, Peter Bell, Steve Renals
We propose a multi-scale octave convolution layer to learn robust speech representations efficiently. Octave convolutions were introduced by Chen et al [1] in the computer vision f…
Cross lingual transfer learning for zero-resource domain adaptation
Alberto Abad, Peter Bell, Andrea Carmantini +1
We propose a method for zero-resource domain adaptation of DNN acoustic models, for use in low-resource situations where the only in-language training data available may be poorly…
Channel adversarial training for speaker verification and diarization
Chau Luu, Peter Bell, Steve Renals
Previous work has encouraged domain-invariance in deep speaker embedding by adversarially classifying the dataset or labelled environment to which the generated features belong. We…
Speaker Adaptive Training using Model Agnostic Meta-Learning
Ondřej Klejch, Joachim Fainberg, Peter Bell +1
Speaker adaptive training (SAT) of neural network acoustic models learns models in a way that makes them more suitable for adaptation to test conditions. Conventionally, model-base…
Acoustic Model Adaptation from Raw Waveforms with SincNet
Joachim Fainberg, Ondřej Klejch, Erfan Loweimi +2
Raw waveform acoustic modelling has recently gained interest due to neural networks' ability to learn feature extraction, and the potential for finding better representations for a…
Embeddings for DNN speaker adaptive training
Joanna Rownicka, Peter Bell, Steve Renals
In this work, we investigate the use of embeddings for speaker-adaptive training of DNNs (DNN-SAT) focusing on a small amount of adaptation data per speaker. DNN-SAT can be viewed…