A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf
.
Combining Non-Pathological Data of Different Language Varieties to Improve DNN-HMM Performance on Pathological Speech
2016
Interspeech 2016
Research on automatic speech recognition (ASR) of pathological speech is particularly hindered by scarce in-domain data resources. Collecting representative pathological speech data is difficult due to the large variability caused by the nature and severity of the disorders, and the rigorous ethical and medical permission requirements. This task becomes even more challenging for languages which have fewer resources, fewer speakers and fewer patients than English, such as the mid-sized language
doi:10.21437/interspeech.2016-109
dblp:conf/interspeech/YilmazGCS16
fatcat:ygz5khmp7vfjdbvqnjjzj56uve