Recently, the speech community is seeing a significant trend of moving from deep neural
network based hybrid modeling to end-to-end (E2E) modeling for automatic speech …

Although supervised deep learning has revolutionized speech and audio processing, it has
necessitated the building of specialist models for individual tasks and application scenarios …

Spoken Language Understanding infers semantic meaning directly from audio data, and
thus promises to reduce error propagation and misunderstandings in end-user applications …

Deep learning has become a predominant method for solving data analysis problems in
virtually all fields of science and engineering. The increasing complexity and the large …

Self-and semi-supervised learning methods have been actively investigated to reduce
labeled training data or enhance model performance. However, these approaches mostly …

The performance of automatic speech recognition (ASR) systems typically degrades
significantly when the training and test data domains are mismatched. In this paper, we …

Speech data from different domains has distinct acoustic and linguistic characteristics. It is
common to train a single multidomain model such as a Conformer transducer for speech …

Speaker adaptation techniques provide a powerful solution to customise automatic speech
recognition (ASR) systems for individual users. Practical application of unsupervised model …

In this paper, several works are proposed to address practi-cal challenges for deploying
RNN Transducer (RNN-T) based speech recognition systems. These challenges are …

It is now well-known that automatic speaker verification (ASV) systems can be spoofed using
various types of adversaries. The usual approach to counteract ASV systems against such …