158 citations · 895 across the 103 of their papers we have counts for
51 papers · 1 filter
EEG-Derived Voice Signature for Attended Speaker Detection
Hongxu Zhu, Siqi Cai, Yidi Jiang +2
\textit{Objective:} Conventional EEG-based auditory attention detection (AAD) is achieved by comparing the time-varying speech stimuli and the elicited EEG signals. However, in ord…
NeuroHeed: Neuro-Steered Speaker Extraction using EEG Signals
Zexu Pan, Marvin Borsdorf, Siqi Cai +2
Humans possess the remarkable ability to selectively attend to a single speaker amidst competing voices and background noise, known as selective auditory attention. Recent studies…
Self-Supervised Acoustic Word Embedding Learning via Correspondence Transformer Encoder
Jingru Lin, Xianghu Yue, Junyi Ao +1
Acoustic word embeddings (AWEs) aims to map a variable-length speech segment into a fixed-dimensional representation. High-quality AWEs should be invariant to variations, such as d…
High-Quality Automatic Voice Over with Accurate Alignment: Supervision through Self-Supervised Discrete Speech Units
Junchen Lu, Berrak Sisman, Mingyang Zhang +1
The goal of Automatic Voice Over (AVO) is to generate speech in sync with a silent video given its text script. Recent AVO frameworks built upon text-to-speech synthesis (TTS) have…
Accented Text-to-Speech Synthesis with Limited Data
Xuehao Zhou, Mingyang Zhang, Yi Zhou +2
This paper presents an accented text-to-speech (TTS) synthesis framework with limited training data. We study two aspects concerning accent rendering: phonetic (phoneme difference)…
TTS-Guided Training for Accent Conversion Without Parallel Data
Yi Zhou, Zhizheng Wu, Mingyang Zhang +2
Accent Conversion (AC) seeks to change the accent of speech from one (source) to another (target) while preserving the speech content and speaker identity. However, many AC approac…
I4U System Description for NIST SRE'20 CTS Challenge
Kong Aik Lee, Tomi Kinnunen, Daniele Colibro +23
This manuscript describes the I4U submission to the 2020 NIST Speaker Recognition Evaluation (SRE'20) Conversational Telephone Speech (CTS) Challenge. The I4U's submission was resu…
Self-Supervised Training of Speaker Encoder with Multi-Modal Diverse Positive Pairs
Ruijie Tao, Kong Aik Lee, Rohan Kumar Das +2
We study a novel neural architecture and its training strategies of speaker encoder for speaker recognition without using any identity labels. The speaker encoder is trained to ext…
Mixed-EVC: Mixed Emotion Synthesis and Control in Voice Conversion
Kun Zhou, Berrak Sisman, Carlos Busso +2
Emotional voice conversion (EVC) traditionally targets the transformation of spoken utterances from one emotional state to another, with previous research mainly focusing on discre…
The Kriston AI System for the VoxCeleb Speaker Recognition Challenge 2022
Qutang Cai, Guoqiang Hong, Zhijian Ye +2
This technical report describes our system for track 1, 2 and 4 of the VoxCeleb Speaker Recognition Challenge 2022 (VoxSRC-22). By combining several ResNet variants, our submission…
The 2022 Far-field Speaker Verification Challenge: Exploring domain mismatch and semi-supervised learning under the far-field scenario
Xiaoyi Qin, Ming Li, Hui Bu +2
FFSVC2022 is the second challenge of far-field speaker verification. FFSVC2022 provides the fully-supervised far-field speaker verification to further explore the far-field scenari…
LightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERT
Rui Wang, Qibing Bai, Junyi Ao +6
Self-supervised speech representation learning has shown promising results in various speech processing tasks. However, the pre-trained models, e.g., HuBERT, are storage-intensive…
HLT-NUS SUBMISSION FOR 2020 NIST Conversational Telephone Speech SRE
Rohan Kumar Das, Ruijie Tao, Haizhou Li
This work provides a brief description of Human Language Technology (HLT) Laboratory, National University of Singapore (NUS) system submission for 2020 NIST conversational telephon…
DeepA: A Deep Neural Analyzer For Speech And Singing Vocoding
Sergey Nikonorov, Berrak Sisman, Mingyang Zhang +1
Conventional vocoders are commonly used as analysis tools to provide interpretable features for downstream tasks such as speech synthesis and voice conversion. They are built under…
Disentanglement of Emotional Style and Speaker Identity for Expressive Voice Conversion
Zongyang Du, Berrak Sisman, Kun Zhou +1
Expressive voice conversion performs identity conversion for emotional speakers by jointly converting speaker identity and emotional style. Due to the hierarchical structure of spe…
Self-supervised Speaker Recognition with Loss-gated Learning
Ruijie Tao, Kong Aik Lee, Rohan Kumar Das +2
In self-supervised learning for speaker recognition, pseudo labels are useful as the supervision signals. It is a known fact that a speaker recognition model doesn't always benefit…
VisualTTS: TTS with Accurate Lip-Speech Synchronization for Automatic Voice Over
Junchen Lu, Berrak Sisman, Rui Liu +2
In this paper, we formulate a novel task to synthesize speech in sync with a silent pre-recorded video, denoted as automatic voice over (AVO). Unlike traditional speech synthesis,…
USEV: Universal Speaker Extraction with Visual Cue
Zexu Pan, Meng Ge, Haizhou Li
A speaker extraction algorithm seeks to extract the target speaker's speech from a multi-talker speech mixture. The prior studies focus mostly on speaker extraction from a highly o…
Is Someone Speaking? Exploring Long-term Temporal Features for Audio-visual Active Speaker Detection
Ruijie Tao, Zexu Pan, Rohan Kumar Das +3
Active speaker detection (ASD) seeks to detect who is speaking in a visual scene of one or more speakers. The successful ASD depends on accurate interpretation of short-term and lo…
Expressive Voice Conversion: A Joint Framework for Speaker Identity and Emotional Style Transfer
Zongyang Du, Berrak Sisman, Kun Zhou +1
Traditional voice conversion(VC) has been focused on speaker identity conversion for speech with a neutral expression. We note that emotional expression plays an essential role in…
Selective Listening by Synchronizing Speech with Lips
Zexu Pan, Ruijie Tao, Chenglin Xu +1
A speaker extraction algorithm seeks to extract the speech of a target speaker from a multi-talker speech mixture when given a cue that represents the target speaker, such as a pre…
The Multi-speaker Multi-style Voice Cloning Challenge 2021
Qicong Xie, Xiaohai Tian, Guanghou Liu +9
The Multi-speaker Multi-style Voice Cloning Challenge (M2VoC) aims to provide a common sizable dataset as well as a fair testbed for the benchmarking of the popular voice cloning t…
Target Speaker Verification with Selective Auditory Attention for Single and Multi-talker Speech
Chenglin Xu, Wei Rao, Jibin Wu +1
Speaker verification has been studied mostly under the single-talker condition. It is adversely affected in the presence of interference speakers. Inspired by the study on target s…
Data Augmentation with Signal Companding for Detection of Logical Access Attacks
Rohan Kumar Das, Jichen Yang, Haizhou Li
The recent advances in voice conversion (VC) and text-to-speech (TTS) make it possible to produce natural sounding speech that poses threat to automatic speaker verification (ASV)…