Despite the rapid progress of automatic speech recognition (ASR) technologies
targeting normal speech in recent decades, accurate recognition of dysarthric
and elderly speech remains highly challenging tasks to date. Sources of
heterogeneity commonly found in normal speech including accent or gender, when
further compounded with the variability over age and speech pathology severity
level, create large diversity among speakers. To this end, speaker adaptation
techniques play a key role in personalization of ASR systems for such users.
Motivated by the spectro-temporal level differences between dysarthric, elderly
and normal speech that systematically manifest in articulatory imprecision,
decreased volume and clarity, slower speaking rates and increased dysfluencies,
novel spectrotemporal subspace basis deep embedding features derived using SVD
speech spectrum decomposition are proposed in this paper to facilitate
auxiliary feature based speaker adaptation of state-of-the-art hybrid DNN/TDNN
and end-to-end Conformer speech recognition systems. Experiments were conducted
on four tasks: the English UASpeech and TORGO dysarthric speech corpora; the
English DementiaBank Pitt and Cantonese JCCOCC MoCA elderly speech datasets.
The proposed spectro-temporal deep feature adapted systems outperformed
baseline i-Vector and xVector adaptation by up to 2.63% absolute (8.63%
relative) reduction in word error rate (WER). Consistent performance
improvements were retained after model based speaker adaptation using learning
hidden unit contributions (LHUC) was further applied. The best speaker adapted
system using the proposed spectral basis embedding features produced the lowest
published WER of 25.05% on the UASpeech test set of 16 dysarthric speakers.

该研究提出了一种基于频谱分解的深度特征提取方法，用于通过个性化的演讲者自适应技术提高自动语音识别系统对肌肉发音障碍者和老年人的识别准确率。通过在四项任务中的实验表明，在应用该技术后，识别准确率有了显著的提高。

使用光谱时域深度特征进行言语适应，在语音识别中应用于发音困难和老年人

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

Automatic recognition of disordered speech remains a highly challenging task
to date. Sources of variability commonly found in normal speech including
accent, age or gender, when further compounded with the underlying causes of
speech impairment and varying severity levels, create large diversity among
speakers. To this end, speaker adaptation techniques play a vital role in
current speech recognition systems. Motivated by the spectro-temporal level
differences between disordered and normal speech that systematically manifest
in articulatory imprecision, decreased volume and clarity, slower speaking
rates and increased dysfluencies, novel spectro-temporal subspace basis
embedding deep features derived by SVD decomposition of speech spectrum are
proposed to facilitate both accurate speech intelligibility assessment and
auxiliary feature based speaker adaptation of state-of-the-art hybrid DNN and
end-to-end disordered speech recognition systems. Experiments conducted on the
UASpeech corpus suggest the proposed spectro-temporal deep feature adapted
systems consistently outperformed baseline i-Vector adaptation by up to 2.63%
absolute (8.6% relative) reduction in word error rate (WER) with or without
data augmentation. Learning hidden unit contribution (LHUC) based speaker
adaptation was further applied. The final speaker adapted system using the
proposed spectral basis embedding features gave an overall WER of 25.6% on the
UASpeech test set of 16 dysarthric speakers

本文研究基于 SVD 分解的深度学习算法，用于自动识别语音障碍人士的语音，并且使用该算法提高了基于语音识别的自适应技术在该领域中的性能。