Over the past decades, a tremendous amount of research has been done on the use of
machine learning for speech processing applications, especially speech recognition …

Eliminating the negative effect of non-stationary environmental noise is a long-standing
research topic for automatic speech recognition but still remains an important challenge …

Speech enhancement and automatic speech recognition (ASR) are most often evaluated in
matched (or multi-condition) settings where the acoustic conditions of the training data …

Transfer learning is a vital technique that generalizes models trained for one setting or task
to other settings or tasks. For example in speech recognition, an acoustic model trained for …

We present a structured overview of adaptation algorithms for neural network-based speech
recognition, considering both hybrid hidden Markov model/neural network systems and end …

This paper proposes a simple yet effective model-based neural network speaker adaptation
technique that learns speaker-specific hidden unit contributions given adaptation data …

A major advantage of statistical parametric speech synthesis (SPSS) over unit-selection
speech synthesis is its adaptability and controllability in changing speaker characteristics …

This work presents a broad study on the adaptation of neural network acoustic models by
means of learning hidden unit contributions (LHUC)-a method that linearly re-combines …

In acoustic modeling, speaker adaptive training (SAT) has been a long-standing technique
for the traditional Gaussian mixture models (GMMs). Acoustic models trained with SAT …

We propose an integrated end-to-end automatic speech recognition (ASR) paradigm by joint
learning of the front-end speech signal processing and back-end acoustic modeling. We …