Publications

[Abstract] This paper introduces an end-to-end neural speech restoration model, HD-DEMUCS, demonstrating efficacy across multiple distortion environments. Unl...

[Abstract] We introduce Multi-level feature Fusion-based Periodicity Analysis Model (MF-PAM), a novel deep learning-based pitch estimation model that accurate...

[Abstract] The goal of this work is zero-shot text-to-speech synthesis, with speaking styles and voices learnt from facial characteristics. Inspired by the na...

[Abstract] Multi-lingual speech recognition aims to distinguish linguistic expressions in different languages and integrate acoustic processing simultaneously...

[Abstract] Enhancing speech quality is an indispensable yet difficult task as it is often complicated by a range of degradation factors. In addition to additi...

[Abstract] We propose a new single channel source separation method based on score-matching of a stochastic differential equation (SDE). We craft a tailored c...

[Abstract] In this paper, we propose a novel end-to-end user-defined keyword spotting method that utilizes linguistically corresponding patterns between speec...

[Abstract] The first spoofing-aware speaker verification (SASV) challenge aims to integrate research efforts in speaker verification and anti-spoofing. We ext...

[Abstract] Deep learning has brought impressive progress in the study of both automatic speaker verification (ASV) and spoofing countermeasures (CM). Although...

[Abstract] Modern neural speech enhancement models usually include various forms of phase information in their training loss terms, either explicitly or impli...

[Abstract] In this work, we present a novel audio-visual dataset for active speaker detection in the wild. A speaker is considered active when his or her face...

[Abstract] In this paper, we address the problem of separating individual speech signals from videos using audio-visual neural processing. Most conventional a...

[Abstract] The goal of this work is to synchronise audio and video of a talking face using deep neural network models. Existing works have trained networks on...

[Abstract] In this paper, we propose a system to extract effective speaker representations from a speech signal using a deep learning method. Based on the fac...

[Abstract] Many approaches can derive information about a single speaker’s identity from the speech by learning to recognize consistent characteristics of aco...

[Abstract] In this paper, we propose an effective training strategy to extract robust speaker representations from a speech signal. One of the key challenges ...

[Abstract] The objective of this paper is to separate a target speaker’s speech from a mixture of two speakers using a deep audio-visual speech separation net...

[Abstract] The goal of this work is to train discriminative cross-modal embeddings without access to manually annotated data. Recent advances in self-supervis...

[Abstract] This paper proposes a new strategy for learning effective cross-modal joint embeddings using self-supervision. We set up the problem as one of cros...

[Abstract] In this paper, we propose an effective active learning query strategy for an automatic speech recognition system with the aim of reducing the train...

[Abstract] This paper proposes a new strategy for learning powerful cross-modal embeddings for audio-to-video synchronisation. Here, we set up the problem as ...

[Abstract] In this letter, a generic search grid generation algorithm for far-field source localization (SL) is proposed. Since conventional uniform regular g...

[Abstract] This paper presents a 3-D search grid allocation algorithm for source localization and beam steering. Data-driven beam-steering systems can benefit...