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 …

The machine recognition of speech spoken at a distance from the microphones, known as
far-field automatic speech recognition (ASR), has received a significant increase in attention …

The external language models (LM) integration remains a challenging task for end-to-end
(E2E) automatic speech recognition (ASR) which has no clear division between acoustic …

Beamforming has been extensively investigated for multi-channel audio processing tasks.
Recently, learning-based beamforming methods, sometimes called neural beamformers …

As the most widely-used spatial filtering approach for multi-channel speech separation,
beamforming extracts the target speech signal arriving from a specific direction. An …

Domain mismatch between training and testing can lead to significant degradation in
performance in many machine learning scenarios. Unfortunately, this is not a rare situation …

Machine lipreading is a special type of automatic speech recognition (ASR) which
transcribes human speech by visually interpreting the movement of related face regions …

We propose a novel adversarial multi-task learning scheme, aiming at actively curtailing the
inter-talker feature variability while maximizing its senone discriminability so as to enhance …

The teacher-student (T/S) learning has been shown to be effective for a variety of problems
such as domain adaptation and model compression. One shortcoming of the T/S learning is …

The efficacy of external language model (LM) integration with existing end-to-end (E2E)
automatic speech recognition (ASR) systems can be improved significantly using the …