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20202026
most citedPersonalized Adversarial Data Augmentation for Dysarthric and Elderly Speech Recognition

38 citations · 63 across the 44 of their papers we have counts for

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28 papers · 1 filter

eess.AS2026

Decoding while Adapting: Zero-Shot Online Speaker Adaptation via Audio-Textual Prompts for Elderly Speech Recognition

Chengxi Deng, Xurong Xie, Shujie Hu +7

This paper proposes a novel cross-utterance audio-textual prompts based speaker adaptation approach for elderly speech recognition. It enables zero-shot, real-time adaptation to un…

eess.AS2026

UNISON: A Unified Sound Generation and Editing Framework via Deep LLM Fusion

Zhaoqing Li, Haoning Xu, Jingran Su +9

We present UNISON, a latent diffusion framework that unifies speech generation, sound generation, and audio editing within a single model. A single model handles text-to-audio, tex…

eess.AS2026

ACAVCaps: Enabling large-scale training for fine-grained and diverse audio understanding

Yadong Niu, Tianzi Wang, Heinrich Dinkel +6

General audio understanding is a fundamental goal for large audio-language models, with audio captioning serving as a cornerstone task for their development. However, progress in t…

eess.AS2025

Towards Effective and Efficient Non-autoregressive decoders for Conformer and LLM-based ASR using Block-based Attention Mask

Tianzi Wang, Xurong Xie, Zengrui Jin +9

Automatic speech recognition (ASR) systems often rely on autoregressive (AR) Transformer decoder architectures, which limit efficient inference parallelization due to their sequent…

eess.AS2025

Exploring Cross-Utterance Speech Contexts for Conformer-Transducer Speech Recognition Systems

Mingyu Cui, Mengzhe Geng, Jiajun Deng +8

This paper investigates four types of cross-utterance speech contexts modeling approaches for streaming and non-streaming Conformer-Transformer (C-T) ASR systems: i) input audio fe…

eess.AS2025

MECAT: A Multi-Experts Constructed Benchmark for Fine-Grained Audio Understanding Tasks

Yadong Niu, Tianzi Wang, Heinrich Dinkel +7

While large audio-language models have advanced open-ended audio understanding, they still fall short of nuanced human-level comprehension. This gap persists largely because curren…

eess.AS2024

Structured Speaker-Deficiency Adaptation of Foundation Models for Dysarthric and Elderly Speech Recognition

Shujie Hu, Xurong Xie, Mengzhe Geng +8

Data-intensive fine-tuning of speech foundation models (SFMs) to scarce and diverse dysarthric and elderly speech leads to data bias and poor generalization to unseen speakers. Thi…

eess.AS2024

Self-supervised ASR Models and Features For Dysarthric and Elderly Speech Recognition

Shujie Hu, Xurong Xie, Mengzhe Geng +8

Self-supervised learning (SSL) based speech foundation models have been applied to a wide range of ASR tasks. However, their application to dysarthric and elderly speech via data-i…

eess.AS2024

Perceiver-Prompt: Flexible Speaker Adaptation in Whisper for Chinese Disordered Speech Recognition

Yicong Jiang, Tianzi Wang, Xurong Xie +7

Disordered speech recognition profound implications for improving the quality of life for individuals afflicted with, for example, dysarthria. Dysarthric speech recognition encount…

eess.AS2023

Towards Automatic Data Augmentation for Disordered Speech Recognition

Zengrui Jin, Xurong Xie, Tianzi Wang +5

Automatic recognition of disordered speech remains a highly challenging task to date due to data scarcity. This paper presents a reinforcement learning (RL) based on-the-fly data a…

eess.AS2023★ 1 cited

Audio-visual End-to-end Multi-channel Speech Separation, Dereverberation and Recognition

Guinan Li, Jiajun Deng, Mengzhe Geng +6

Accurate recognition of cocktail party speech containing overlapping speakers, noise and reverberation remains a highly challenging task to date. Motivated by the invariance of vis…

eess.AS2023

Hyper-parameter Adaptation of Conformer ASR Systems for Elderly and Dysarthric Speech Recognition

Tianzi Wang, Shoukang Hu, Jiajun Deng +5

Automatic recognition of disordered and elderly speech remains highly challenging tasks to date due to data scarcity. Parameter fine-tuning is often used to exploit the large quant…

eess.AS2023

Factorised Speaker-environment Adaptive Training of Conformer Speech Recognition Systems

Jiajun Deng, Guinan Li, Xurong Xie +6

Rich sources of variability in natural speech present significant challenges to current data intensive speech recognition technologies. To model both speaker and environment level…

eess.AS2023

Use of Speech Impairment Severity for Dysarthric Speech Recognition

Mengzhe Geng, Zengrui Jin, Tianzi Wang +7

A key challenge in dysarthric speech recognition is the speaker-level diversity attributed to both speaker-identity associated factors such as gender, and speech impairment severit…

eess.AS2023

Confidence Score Based Speaker Adaptation of Conformer Speech Recognition Systems

Jiajun Deng, Xurong Xie, Tianzi Wang +6

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

eess.AS2022★ 1 cited

Adversarial Data Augmentation Using VAE-GAN for Disordered Speech Recognition

Zengrui Jin, Xurong Xie, Mengzhe Geng +5

Automatic recognition of disordered speech remains a highly challenging task to date. The underlying neuro-motor conditions, often compounded with co-occurring physical disabilitie…

eess.AS2022

Confidence Score Based Conformer Speaker Adaptation for Speech Recognition

Jiajun Deng, Xurong Xie, Tianzi Wang +7

A key challenge for automatic speech recognition (ASR) systems is to model the speaker level variability. In this paper, compact speaker dependent learning hidden unit contribution…

eess.AS2022★ 38 cited

Personalized Adversarial Data Augmentation for Dysarthric and Elderly Speech Recognition

Zengrui Jin, Mengzhe Geng, Jiajun Deng +4

Despite the rapid progress of automatic speech recognition (ASR) technologies targeting normal speech, accurate recognition of dysarthric and elderly speech remains highly challeng…

eess.AS2022

Conformer Based Elderly Speech Recognition System for Alzheimer's Disease Detection

Tianzi Wang, Jiajun Deng, Mengzhe Geng +7

Early diagnosis of Alzheimer's disease (AD) is crucial in facilitating preventive care to delay further progression. This paper presents the development of a state-of-the-art Confo…

eess.AS2022★ 6 cited

Two-pass Decoding and Cross-adaptation Based System Combination of End-to-end Conformer and Hybrid TDNN ASR Systems

Mingyu Cui, Jiajun Deng, Shoukang Hu +7

Fundamental modelling differences between hybrid and end-to-end (E2E) automatic speech recognition (ASR) systems create large diversity and complementarity among them. This paper i…

eess.AS2022

Exploiting Cross-domain And Cross-Lingual Ultrasound Tongue Imaging Features For Elderly And Dysarthric Speech Recognition

Shujie Hu, Xurong Xie, Mengzhe Geng +6

Articulatory features are inherently invariant to acoustic signal distortion and have been successfully incorporated into automatic speech recognition (ASR) systems designed for no…

eess.AS2022

Exploiting Cross Domain Acoustic-to-articulatory Inverted Features For Disordered Speech Recognition

Shujie Hu, Shansong Liu, Xurong Xie +6

Articulatory features are inherently invariant to acoustic signal distortion and have been successfully incorporated into automatic speech recognition (ASR) systems for normal spee…

eess.AS2022

Speaker Adaptation Using Spectro-Temporal Deep Features for Dysarthric and Elderly Speech Recognition

Mengzhe Geng, Xurong Xie, Zi Ye +5

Despite the rapid progress of automatic speech recognition (ASR) technologies targeting normal speech in recent decades, accurate recognition of dysarthric and elderly speech remai…

eess.AS2022

On-the-Fly Feature Based Rapid Speaker Adaptation for Dysarthric and Elderly Speech Recognition

Mengzhe Geng, Xurong Xie, Rongfeng Su +7

Accurate recognition of dysarthric and elderly speech remain challenging tasks to date. Speaker-level heterogeneity attributed to accent or gender, when aggregated with age and spe…