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Exploiting Audio-Visual Features with Pretrained AV-HuBERT for Multi-Modal Dysarthric Speech Reconstruction

2024-01-31 12:45:43
Xueyuan Chen, Yuejiao Wang, Xixin Wu, Disong Wang, Zhiyong Wu, Xunying Liu, Helen Meng


Dysarthric speech reconstruction (DSR) aims to transform dysarthric speech into normal speech by improving the intelligibility and naturalness. This is a challenging task especially for patients with severe dysarthria and speaking in complex, noisy acoustic environments. To address these challenges, we propose a novel multi-modal framework to utilize visual information, e.g., lip movements, in DSR as extra clues for reconstructing the highly abnormal pronunciations. The multi-modal framework consists of: (i) a multi-modal encoder to extract robust phoneme embeddings from dysarthric speech with auxiliary visual features; (ii) a variance adaptor to infer the normal phoneme duration and pitch contour from the extracted phoneme embeddings; (iii) a speaker encoder to encode the speaker's voice characteristics; and (iv) a mel-decoder to generate the reconstructed mel-spectrogram based on the extracted phoneme embeddings, prosodic features and speaker embeddings. Both objective and subjective evaluations conducted on the commonly used UASpeech corpus show that our proposed approach can achieve significant improvements over baseline systems in terms of speech intelligibility and naturalness, especially for the speakers with more severe symptoms. Compared with original dysarthric speech, the reconstructed speech achieves 42.1\% absolute word error rate reduction for patients with more severe dysarthria levels.

Abstract (translated)

Dysarthric speech reconstruction (DSR) 的目标是通过提高可听性和自然性来将失调性言语转化为正常言语。这对那些患有严重失调症并在复杂、噪音干扰的听觉环境中说话的患者来说是一个具有挑战性的任务。为了应对这些挑战,我们提出了一个新颖的多模态框架,以利用视觉信息,例如嘴唇运动,作为附加线索来重构高度异常的语调。多模态框架包括:(i)一个多模态编码器,用于从失调性语音中提取有辅助视觉特征的 robust 音素嵌入;(ii)一个方差适应器,用于从提取的音素嵌入中推断正常音素持续时间和语调轮廓;(iii)一个说话者编码器,用于编码说话者的声音特征;(iv)一个 Mel-解码器,根据提取的音素嵌入生成重构的 Mel-频谱图。对于常用的 UASpeech 数据集进行客观和主观评估,结果显示我们提出的方法相对于基线系统在提高语音可听性和自然性方面具有显著的改进,特别是对于症状更加严重的说话者。与原始失调性语音相比,重构的语音在患有更严重失调症的患者上降低了42.1%的绝对单词错误率。



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