55 citations · 117 across the 27 of their papers we have counts for
30 papers
The Multimodal Information Based Speech Processing (MISP) 2025 Challenge: Audio-Visual Diarization and Recognition
Ming Gao, Shilong Wu, Hang Chen +6
Meetings are a valuable yet challenging scenario for speech applications due to complex acoustic conditions. This paper summarizes the outcomes of the MISP 2025 Challenge, hosted a…
As Biased as You Measure: Methodological Pitfalls of Bias Evaluations in Speaker Verification Research
Wiebke Hutiri, Tanvina Patel, Aaron Yi Ding +1
Detecting and mitigating bias in speaker verification systems is important, as datasets, processing choices and algorithms can lead to performance differences that systematically f…
Improving child speech recognition with augmented child-like speech
Yuanyuan Zhang, Zhengjun Yue, Tanvina Patel +1
State-of-the-art ASRs show suboptimal performance for child speech. The scarcity of child speech limits the development of child speech recognition (CSR). Therefore, we studied chi…
Exploring data augmentation in bias mitigation against non-native-accented speech
Yuanyuan Zhang, Aaricia Herygers, Tanvina Patel +2
Automatic speech recognition (ASR) should serve every speaker, not only the majority ``standard'' speakers of a language. In order to build inclusive ASR, mitigating the bias again…
Improving Whispered Speech Recognition Performance using Pseudo-whispered based Data Augmentation
Zhaofeng Lin, Tanvina Patel, Odette Scharenborg
Whispering is a distinct form of speech known for its soft, breathy, and hushed characteristics, often used for private communication. The acoustic characteristics of whispered spe…
The Multimodal Information Based Speech Processing (MISP) 2023 Challenge: Audio-Visual Target Speaker Extraction
Shilong Wu, Chenxi Wang, Hang Chen +13
Previous Multimodal Information based Speech Processing (MISP) challenges mainly focused on audio-visual speech recognition (AVSR) with commendable success. However, the most advan…
Using Data Augmentations and VTLN to Reduce Bias in Dutch End-to-End Speech Recognition Systems
Tanvina Patel, Odette Scharenborg
Speech technology has improved greatly for norm speakers, i.e., adult native speakers of a language without speech impediments or strong accents. However, non-norm or diverse speak…
The Multimodal Information based Speech Processing (MISP) 2022 Challenge: Audio-Visual Diarization and Recognition
Zhe Wang, Shilong Wu, Hang Chen +12
The Multi-modal Information based Speech Processing (MISP) challenge aims to extend the application of signal processing technology in specific scenarios by promoting the research…
Predicting within and across language phoneme recognition performance of self-supervised learning speech pre-trained models
Hang Ji, Tanvina Patel, Odette Scharenborg
In this work, we analyzed and compared speech representations extracted from different frozen self-supervised learning (SSL) speech pre-trained models on their ability to capture a…
Manipulation of oral cancer speech using neural articulatory synthesis
Bence Mark Halpern, Teja Rebernik, Thomas Tienkamp +5
We present an articulatory synthesis framework for the synthesis and manipulation of oral cancer speech for clinical decision making and alleviation of patient stress. Objective an…
Modelling word learning and recognition using visually grounded speech
Danny Merkx, Sebastiaan Scholten, Stefan L. Frank +2
Background: Computational models of speech recognition often assume that the set of target words is already given. This implies that these models do not learn to recognise speech f…
Discovering Phonetic Inventories with Crosslingual Automatic Speech Recognition
Piotr Żelasko, Siyuan Feng, Laureano Moro Velazquez +5
The high cost of data acquisition makes Automatic Speech Recognition (ASR) model training problematic for most existing languages, including languages that do not even have a writt…
The Effectiveness of Time Stretching for Enhancing Dysarthric Speech for Improved Dysarthric Speech Recognition
Luke Prananta, Bence Mark Halpern, Siyuan Feng +1
In this paper, we investigate several existing and a new state-of-the-art generative adversarial network-based (GAN) voice conversion method for enhancing dysarthric speech for imp…
Towards Identity Preserving Normal to Dysarthric Voice Conversion
Wen-Chin Huang, Bence Mark Halpern, Lester Phillip Violeta +2
We present a voice conversion framework that converts normal speech into dysarthric speech while preserving the speaker identity. Such a framework is essential for (1) clinical dec…
AnyoneNet: Synchronized Speech and Talking Head Generation for Arbitrary Person
Xinsheng Wang, Qicong Xie, Jihua Zhu +2
Automatically generating videos in which synthesized speech is synchronized with lip movements in a talking head has great potential in many human-computer interaction scenarios. I…
An Objective Evaluation Framework for Pathological Speech Synthesis
Bence Mark Halpern, Julian Fritsch, Enno Hermann +3
The development of pathological speech systems is currently hindered by the lack of a standardised objective evaluation framework. In this work, (1) we utilise existing detection a…
Pathological voice adaptation with autoencoder-based voice conversion
Marc Illa, Bence Mark Halpern, Rob van Son +2
In this paper, we propose a new approach to pathological speech synthesis. Instead of using healthy speech as a source, we customise an existing pathological speech sample to a new…
Unsupervised Acoustic Unit Discovery by Leveraging a Language-Independent Subword Discriminative Feature Representation
Siyuan Feng, Piotr Żelasko, Laureano Moro-Velázquez +1
This paper tackles automatically discovering phone-like acoustic units (AUD) from unlabeled speech data. Past studies usually proposed single-step approaches. We propose a two-stag…
Quantifying Bias in Automatic Speech Recognition
Siyuan Feng, Olya Kudina, Bence Mark Halpern +1
Automatic speech recognition (ASR) systems promise to deliver objective interpretation of human speech. Practice and recent evidence suggests that the state-of-the-art (SotA) ASRs…
The effectiveness of unsupervised subword modeling with autoregressive and cross-lingual phone-aware networks
Siyuan Feng, Odette Scharenborg
This study addresses unsupervised subword modeling, i.e., learning acoustic feature representations that can distinguish between subword units of a language. We propose a two-stage…
Show and Speak: Directly Synthesize Spoken Description of Images
Xinsheng Wang, Siyuan Feng, Jihua Zhu +2
This paper proposes a new model, referred to as the show and speak (SAS) model that, for the first time, is able to directly synthesize spoken descriptions of images, bypassing the…
The CUHK-TUDELFT System for The SLT 2021 Children Speech Recognition Challenge
Si-Ioi Ng, Wei Liu, Zhiyuan Peng +4
This technical report describes our submission to the 2021 SLT Children Speech Recognition Challenge (CSRC) Track 1. Our approach combines the use of a joint CTC-attention end-to-e…
How Phonotactics Affect Multilingual and Zero-shot ASR Performance
Siyuan Feng, Piotr Żelasko, Laureano Moro-Velázquez +4
The idea of combining multiple languages' recordings to train a single automatic speech recognition (ASR) model brings the promise of the emergence of universal speech representati…
Unsupervised Subword Modeling Using Autoregressive Pretraining and Cross-Lingual Phone-Aware Modeling
Siyuan Feng, Odette Scharenborg
This study addresses unsupervised subword modeling, i.e., learning feature representations that can distinguish subword units of a language. The proposed approach adopts a two-stag…