Publications

2025

1. 

   Detecting Neurocognitive Disorders through Analyses of Topic Evolution and Cross-modal Consistency in Visual-Stimulated Narratives

   Jinchao Li*, Yuejiao Wang*, Junan Li*, Jiawen Kang*, Bo Zheng, Simon Wong, 10 more authors, Helen Meng

   In JSTSP (under review), 2025

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   Early detection of neurocognitive disorders (NCDs) is crucial for timely intervention and disease management. Speech analysis offers a non-intrusive and scalable screening method, particularly through narrative tasks in neuropsychological assessment tools. Traditional narrative analysis often focuses on local indicators in microstructure, such as word usage and syntax. While these features provide insights into language production abilities, they often fail to capture global narrative patterns, or microstructures. Macrostructures include coherence, thematic organization, and logical progressions, reflecting essential cognitive skills potentially critical for recognizing NCDs. Addressing this gap, we propose to investigate specific cognitive and linguistic challenges by analyzing topical shifts, temporal dynamics, and the coherence of narratives over time, aiming to reveal cognitive deficits by identifying narrative impairments, and exploring their impact on communication and cognition. The investigation is based on the CU-MARVEL Rabbit Story corpus, which comprises recordings of a story-telling task from 758 older adults. We developed two approaches: the Dynamic Topic Models (DTM)-based temporal analysis to examine the evolution of topics over time, and the Text-Image Temporal Alignment Network (TITAN) to evaluate the coherence between spoken narratives and visual stimuli. DTM-based approach validated the effectiveness of dynamic topic consistency as a macrostructural metric (F1=0.61, AUC=0.78). The TITAN approach achieved the highest performance (F1=0.72, AUC=0.81), surpassing established microstructural and macrostructural feature sets. Cross-comparison and regression tasks further demonstrated the effectiveness of proposed dynamic macrostructural modeling approaches for NCD detection.

   @inproceedings{li2025detecting,
     url = {https://arxiv.org/abs/2501.03727},
     title = {Detecting Neurocognitive Disorders through Analyses of Topic Evolution and Cross-modal Consistency in Visual-Stimulated Narratives},
     author = {Li*, Jinchao and Wang*, Yuejiao and Li*, Junan and Kang*, Jiawen and Zheng, Bo and Wong, Simon and Mak, Brian and Fung, Helene and Woo, Jean and Mak, Man-Wai and Kwok, Timothy and Mok, Vincent and Gong, Xianmin and Wu, Xixin and Liu, Xunying and Wong, Patrick and Meng, Helen},
     booktitle = {JSTSP (under review)},
     year = {2025},
     organization = {IEEE}
   }

2024

1. 

   emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation

   Ziyang Ma, Zhisheng Zheng, Jiaxin Ye, Jinchao Li, Zhifu Gao, Shiliang Zhang, Xie Chen

   In ACL, 2024

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   We propose emotion2vec, a universal speech emotion representation model. emotion2vec is pre-trained on open-source unlabeled emotion data through self-supervised online distillation, combining utterance-level loss and frame-level loss during pre-training. emotion2vec outperforms state-of-the-art pre-trained universal models and emotion specialist models by only training linear layers for the speech emotion recognition task on the mainstream IEMOCAP dataset. In addition, emotion2vec shows consistent improvements among 10 different languages of speech emotion recognition datasets. emotion2vec also shows excellent results on other emotion tasks, such as song emotion recognition, emotion prediction in conversation, and sentiment analysis. Comparison experiments, ablation experiments, and visualization comprehensively demonstrate the universal capability of the proposed emotion2vec. To the best of our knowledge, emotion2vec is the first universal representation model in various emotion-related tasks, filling a gap in the field.

   @inproceedings{ma2023emotion2vec,
     url = {https://arxiv.org/abs/2312.15185},
     title = {emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation},
     author = {Ma, Ziyang and Zheng, Zhisheng and Ye, Jiaxin and Li, Jinchao and Gao, Zhifu and Zhang, Shiliang and Chen, Xie},
     booktitle = {ACL},
     year = {2024},
     organization = {ACL}
   }

2023

1. 

   Leveraging Pretrained Representations With Task-Related Keywords for Alzheimer’s Disease Detection

   Jinchao Li, Kaitao Song, Junan Li, Bo Zheng, Dongsheng Li, Xixin Wu, Xunying Liu, Helen Meng

   In ICASSP, 2023

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   With the global population aging rapidly, Alzheimer’s disease (AD) is particularly prominent in older adults, which has an insidious onset and leads to a gradual, irreversible deterioration in cognitive domains (memory, communication, etc.). Speech-based AD detection opens up the possibility of widespread screening and timely disease intervention. Recent advances in pre-trained models motivate AD detection modeling to shift from low-level features to high-level representations. This paper presents several efficient methods to extract better AD-related cues from high-level acoustic and linguistic features. Based on these features, the paper also proposes a novel task-oriented approach by modeling the relationship between the participants’ description and the cognitive task. Experiments are carried out on the ADReSS dataset in a binary classification setup, and models are evaluated on the unseen test set. Results and comparison with recent literature demonstrate the efficiency and superior performance of proposed acoustic, linguistic and task-oriented methods. The findings also show the importance of semantic and syntactic information, and feasibility of automation and generalization with the promising audio-only and task-oriented methods for the AD detection task.

   @inproceedings{li2023leveraging,
     url = {https://ieeexplore.ieee.org/document/10096205},
     title = {Leveraging Pretrained Representations With Task-Related Keywords for Alzheimer’s Disease Detection},
     author = {Li, Jinchao and Song, Kaitao and Li, Junan and Zheng, Bo and Li, Dongsheng and Wu, Xixin and Liu, Xunying and Meng, Helen},
     booktitle = {ICASSP},
     year = {2023},
     organization = {IEEE}
   }

2. 

   A Hierarchical Regression Chain Framework for Affective Vocal Burst Recognition

   Jinchao Li, Xixin Wu, Kaitao Song, Dongsheng Li, Xunying Liu, Helen Meng

   In ICASSP, 2023

   ABS BIB PDF POSTER CODE

   As a common way of emotion signaling via non-linguistic vocalizations, vocal burst (VB) plays an important role in daily social interaction. Understanding and modeling human vocal bursts are indispensable for developing robust and general artificial intelligence. Exploring computational approaches for understanding vocal bursts is attracting increasing research attention. In this work, we propose a hierarchical framework, based on chain regression models, for affective recognition from VBs, that explicitly considers multiple relationships: (i) between emotional states and diverse cultures; (ii) between low-dimensional (arousal & valence) and high-dimensional (10 emotion classes) emotion spaces; and (iii) between various emotion classes within the high-dimensional space. To address the challenge of data sparsity, we also use self-supervised learning (SSL) representations with layer-wise and temporal aggregation modules. The proposed systems participated in the ACII Affective Vocal Burst (A-VB) Challenge 2022 and ranked first in the "TWO” and "CULTURE” tasks. Experimental results based on the ACII Challenge 2022 dataset demonstrate the superior performance of the proposed system and the effectiveness of considering multiple relationships using hierarchical regression chain models.

   @inproceedings{li2023hierarchical,
     url = {https://ieeexplore.ieee.org/document/10096395/},
     title = {A Hierarchical Regression Chain Framework for Affective Vocal Burst Recognition},
     author = {Li, Jinchao and Wu, Xixin and Song, Kaitao and Li, Dongsheng and Liu, Xunying and Meng, Helen},
     booktitle = {ICASSP},
     year = {2023},
     organization = {IEEE}
   }

2022

1. 

   Context-Aware Multimodal Fusion for Emotion Recognition

   Jinchao Li, Shuai Wang, Yang Chao, Xunying Liu, Helen Meng

   In INTERSPEECH, 2022

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   Automatic emotion recognition (AER) is an inherently complex multimodal task that aims to automatically determine the emotional state of a given expression. Recent works have witnessed the benefits of upstream pretrained models in both audio and textual modalities for the AER task. However, efforts are still needed to effectively integrate features across multiple modalities, devoting due considerations to granularity mismatch and asynchrony in time steps. In this work, we first validate the effectiveness of the upstream models in a unimodal setup and empirically find that partial fine-tuning of the pretrained model in the feature space can significantly boost performance. Moreover, we take the context of the current sentence to model a more accurate emotional state. Based on the unimodal setups, we further propose several multimodal fusion methods to combine high-level features from the audio and text modalities. Experiments are carried out on the IEMOCAP dataset in a 4-category classification problem and compared with state-of-the-art methods in recent literature. Results show that the proposed models gave a superior performance of up to 84.45% and 80.36% weighted accuracy scores respectively in Session 5 and 5-fold cross-validation settings.

   @inproceedings{li2022context,
     url = {https://www.isca-speech.org/archive/interspeech_2022/li22v_interspeech.html},
     title = {Context-Aware Multimodal Fusion for Emotion Recognition},
     author = {Li, Jinchao and Wang, Shuai and Chao, Yang and Liu, Xunying and Meng, Helen},
     booktitle = {INTERSPEECH},
     year = {2022},
     organization = {IEEE}
   }

2021

1. 

   A Comparative Study of Acoustic and Linguistic Features Classification for Alzheimer’s Disease Detection

   Jinchao Li, Jianwei Yu, Zi Ye, Simon Wong, Manwai Mak, Brian Mak, Xunying Liu, Helen Meng

   In ICASSP, 2021

   ABS BIB PDF POSTER CODE

   With the global population ageing rapidly, Alzheimer’s Disease (AD) is particularly prominent in older adults, which has an insidious onset followed by gradual, irreversible deterioration in cognitive domains (memory, communication, etc). Thus the detection of Alzheimer’s Disease is crucial for timely intervention to slow down disease progression. This paper presents a comparative study of different acoustic and linguistic features for the AD detection using various classifiers. Experimental results on ADReSS dataset reflect that the proposed models using ComParE, X-vector, Linguistics, TFIDF and BERT features are able to detect AD with high accuracy and sensitivity, and are comparable with the state-of-the-art results reported. While most previous work used manual transcripts, our results also indicate that similar or even better performance could be obtained using automatically recognized transcripts over manually collected ones. This work achieves accuracy scores at 0.67 for acoustic features and 0.88 for linguistic features on either manual or ASR transcripts on the ADReSS Challenge test set.

   @inproceedings{li2021comparative,
     url = {https://ieeexplore.ieee.org/document/9414147},
     title = {A Comparative Study of Acoustic and Linguistic Features Classification for Alzheimer's Disease Detection},
     author = {Li, Jinchao and Yu, Jianwei and Ye, Zi and Wong, Simon and Mak, Manwai and Mak, Brian and Liu, Xunying and Meng, Helen},
     booktitle = {ICASSP},
     year = {2021},
     organization = {IEEE}
   }

2. 

   Development of the CUHK Elderly Speech Recognition System for Neurocognitive Disorder Detection Using the DementiaBank Corpus

   Zi Ye, Shoukang Hu, Jinchao Li, Xurong Xie, Mengzhe Geng, Jianwei Yu, 4 more authors, Helen Meng

   In ICASSP, 2021

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   Early diagnosis of Neurocognitive Disorder (NCD) is crucial in facilitating preventive care and timely treatment to delay further progression. This paper presents the development of a state-of-the-art automatic speech recognition (ASR) system built on the Dementia-Bank Pitt corpus for automatic NCD detection. Speed perturbation based audio data augmentation expanded the limited elderly speech data by four times. Large quantities of out-of-domain, non-aged adult speech were exploited by cross-domain adapting a 1000-hour LibriSpeech corpus trained LF-MMI factored TDNN system to DementiaBank. The variability among elderly speakers was modeled using i-Vector and learning hidden unit contributions (LHUC) based speaker adaptive training. Robust Bayesian estimation of TDNN systems and LHUC transforms were used in both cross-domain and speaker adaptation. A Transformer language model was also built to improve the final system performance. A word error rate (WER) reduction of 11.72% absolute (26.11% relative) was obtained over the baseline i-Vector adapted LF-MMI TDNN system on the evaluation data of 48 elderly speakers. The best NCD detection accuracy of 88%, comparable to that using the ground truth speech transcripts, was obtained using the textual features extracted from the final ASR system outputs.

   @inproceedings{ye2021development,
     url = {https://ieeexplore.ieee.org/document/9413634},
     title = {Development of the CUHK Elderly Speech Recognition System for Neurocognitive Disorder Detection Using the DementiaBank Corpus},
     author = {Ye, Zi and Hu, Shoukang and Li, Jinchao and Xie, Xurong and Geng, Mengzhe and Yu, Jianwei and Xu, Junhao and Xue, Boyang and Liu, Shansong and Liu, Xunying and Meng, Helen},
     booktitle = {ICASSP},
     year = {2021},
     organization = {IEEE}
   }

2019

1. 

   Method, Device and Electronic Equipment for Determining Sound Source Information based on Microphone Array

   Changbao Zhu, Jinchao Li

   In Patent: CN110148422B, 2019

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   本公开实施例公开了一种基于传声器阵列确定声源信息的方法,其中,方法包括:确定传声器阵列采集的多路音频信号;确定所述多路音频信号的相似性度量信息;确定所述多路音频信号的相关性度量信息;基于所述相似性度量信息和所述相关性度量信息,确定声源信息.还公开了一种基于传声器阵列确定声源信息的装置,其中,装置包括:音频信号确定模块,相似性度量信息确定模块,相关性度量信息确定模块和声源信息确定模块.本公开实施例可以通过确定并基于传声器阵列采集的多路音频信号的相似性度量信息和相关性度量信息,确定声源信息,可以通过传声器阵列得到声源信息,方便后续进行行声源数量估计,可以保证声源数量估计的准确性.