Design, construction and evaluation of emotional multimodal pathological speech database
This addresses the problem of limited data for studying emotional expression in dysarthria patients, though it is incremental as it focuses on creating a new database rather than advancing methods.
The authors tackled the lack of an emotional pathological speech database by constructing the first Chinese multimodal database with 29 controls and 39 dysarthria patients expressing four emotions, achieving average recognition accuracies of 78% for controls and 60% for patients in audio data.
The lack of an available emotion pathology database is one of the key obstacles in studying the emotion expression status of patients with dysarthria. The first Chinese multimodal emotional pathological speech database containing multi-perspective information is constructed in this paper. It includes 29 controls and 39 patients with different degrees of motor dysarthria, expressing happy, sad, angry and neutral emotions. All emotional speech was labeled for intelligibility, types and discrete dimensional emotions by developed WeChat mini-program. The subjective analysis justifies from emotion discrimination accuracy, speech intelligibility, valence-arousal spatial distribution, and correlation between SCL-90 and disease severity. The automatic recognition tested on speech and glottal data, with average accuracy of 78% for controls and 60% for patients in audio, while 51% for controls and 38% for patients in glottal data, indicating an influence of the disease on emotional expression.