In the last decade of automatic speech recognition (ASR) research, the introduction of deep
learning has brought considerable reductions in word error rate of more than 50% relative …

Given the recent surge in developments of deep learning, this paper provides a review of the
state-of-the-art deep learning techniques for audio signal processing. Speech, music, and …

Deep learning is progressively gaining popularity as a viable alternative to i-vectors for
speaker recognition. Promising results have been recently obtained with Convolutional …

This paper introduces WaveNet, a deep neural network for generating raw audio waveforms.
The model is fully probabilistic and autoregressive, with the predictive distribution for each …

This paper introduces WaveNet, a deep neural network for generating raw audio waveforms.
The model is fully probabilistic and autoregressive, with the predictive distribution for each …

We consider the task of unsupervised extraction of meaningful latent representations of
speech by applying autoencoding neural networks to speech waveforms. The goal is to …

Automatic Speech Recognition (ASR), which is aimed to enable natural human–machine
interaction, has been an intensive research area for decades. Many core technologies, such …

Learning acoustic models directly from the raw waveform data with minimal processing is
challenging. Current waveform-based models have generally used very few (~ 2) …

Learning an acoustic model directly from the raw waveform has been an active area of
research. However, waveformbased models have not yet matched the performance of …

The discriminative lexicon is introduced as a mathematical and computational model of the
mental lexicon. This novel theory is inspired by word and paradigm morphology but …