  We present the Chinese Dysarthria Speech Database (CDSD) as a valuable
resource for dysarthria research. This database comprises speech data from 24
participants with dysarthria. Among these participants, one recorded an
additional 10 hours of speech data, while each recorded one hour, resulting in
34 hours of speech material. To accommodate participants with varying cognitive
levels, our text pool primarily consists of content from the AISHELL-1 dataset
and speeches by primary and secondary school students. When participants read
these texts, they must use a mobile device or the ZOOM F8n multi-track field
recorder to record their speeches. In this paper, we elucidate the data
collection and annotation processes and present an approach for establishing a
baseline for dysarthric speech recognition. Furthermore, we conducted a
speaker-dependent dysarthric speech recognition experiment using an additional
10 hours of speech data from one of our participants. Our research findings
indicate that, through extensive data-driven model training, fine-tuning
limited quantities of specific individual data yields commendable results in
speaker-dependent dysarthric speech recognition. However, we observe
significant variations in recognition results among different dysarthric
speakers. These insights provide valuable reference points for
speaker-dependent dysarthric speech recognition.
