  Although the UA-Speech and TORGO databases of control and dysarthric speech
are invaluable resources made available to the research community with the
objective of developing robust automatic speech recognition systems, they have
also been used to validate a considerable number of automatic dysarthric speech
classification approaches. Such approaches typically rely on the underlying
assumption that recordings from control and dysarthric speakers are collected
in the same noiseless environment using the same recording setup. In this
paper, we show that this assumption is violated for the UA-Speech and TORGO
databases. Using voice activity detection to extract speech and non-speech
segments, we show that the majority of state-of-the-art dysarthria
classification approaches achieve the same or a considerably better performance
when using the non-speech segments of these databases than when using the
speech segments. These results demonstrate that such approaches trained and
validated on the UA-Speech and TORGO databases are potentially learning
characteristics of the recording environment or setup rather than dysarthric
speech characteristics. We hope that these results raise awareness in the
research community about the importance of the quality of recordings when
developing and evaluating automatic dysarthria classification approaches.
