A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf
.
Phoneme State Posteriorgram Features for Speech Based Automatic Classification of Speakers in Cold and Healthy Condition
2017
Interspeech 2017
unpublished
We consider the problem of automatically detecting if a speaker is suffering from common cold from his/her speech. When a speaker has symptoms of cold, his/her voice quality changes compared to the normal one. We hypothesize that such a change in voice quality could be reflected in lower likelihoods from a model built using normal speech. In order to capture this, we compute a 120-dimensional posteriorgram feature in each frame using Gaussian mixture model from 120 states of 40 three-states
doi:10.21437/interspeech.2017-1550
fatcat:jeblhdb53zg4bk7p2fxljhfkue