Ustc address -m挑战系统

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2023-06-04 DOI:10.1109/ICASSP49357.2023.10094714

Kangdi Mei, Xinyun Ding, Yinlong Liu, Zhiqiang Guo, Feiyang Xu, Xin Li, Tuya Naren, Jiahong Yuan, Zhenhua Ling

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引用次数: 1

摘要

本文描述了我们向ICASSP 2023信号处理大挑战(SPGC)提交的论文，该挑战侧重于通过自发语音识别多语言阿尔茨海默病(AD)。我们的方法包括使用各种声学特征和与沉默相关的信息进行AD检测和迷你精神状态检查(MMSE)评分预测，并对不同频段的语音进行wav2vec2.0模型微调以用于AD检测。我们在测试数据上的总体结果优于主办方提供的基线，在0-1000Hz频带语音上对双语wav2vec2.0预训练模型进行微调，AD检测准确率达到73.9%，通过融合eGeMAPS和沉默特征，MMSE预测准确率达到4.610 RMSE (r = 0.565)。

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The Ustc System for Adress-m Challenge

This paper describes our submission to the ICASSP 2023 Signal Processing Grand Challenge (SPGC), which focuses on multilingual Alzheimer’s disease (AD) recognition through spontaneous speech. Our approaches include using a variety of acoustic features and silence-related information for AD detection and mini-mental state examination (MMSE) score prediction, and fine-tuning wav2vec2.0 models on speech in various frequency bands for AD detection. Our overall results on the test data outperform the baseline provided by the organizers, achieving 73.9% accuracy in AD detection by fine-tuning our bilingual wav2vec2.0 pre-trained model on the 0-1000Hz frequency band speech, and 4.610 RMSE (r = 0.565) in MMSE prediction through the fusion of eGeMAPS and silence features.

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来源期刊

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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