TY - GEN
T1 - Models for objective evaluation of dysarthric speech from data annotated by multiple listeners
AU - Tu, Ming
AU - Jiao, Yishan
AU - Berisha, Visar
AU - Liss, Julie
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/3/1
Y1 - 2017/3/1
N2 - In subjective evaluation of dysarthric speech, the inter-rater agreement between clinicians can be low. Disagreement among clinicians results from differences in their perceptual assessment abilities, familiarization with a client, clinical experiences, etc. Recently, there has been interest in developing signal processing and machine learning models for objective evaluation of subjective speech quality. In this paper, we propose a new method to address this problem by collecting subjective ratings from multiple evaluators and modeling the reliability of each annotator within a machine learning framework. In contrast to previous work, our model explicitly models the dependence of the speaker on an evaluators reliability. We evaluate the model on a series of experiments on a dysarthric speech database and show that our method outperforms other similar approaches.
AB - In subjective evaluation of dysarthric speech, the inter-rater agreement between clinicians can be low. Disagreement among clinicians results from differences in their perceptual assessment abilities, familiarization with a client, clinical experiences, etc. Recently, there has been interest in developing signal processing and machine learning models for objective evaluation of subjective speech quality. In this paper, we propose a new method to address this problem by collecting subjective ratings from multiple evaluators and modeling the reliability of each annotator within a machine learning framework. In contrast to previous work, our model explicitly models the dependence of the speaker on an evaluators reliability. We evaluate the model on a series of experiments on a dysarthric speech database and show that our method outperforms other similar approaches.
UR - http://www.scopus.com/inward/record.url?scp=85016248707&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85016248707&partnerID=8YFLogxK
U2 - 10.1109/ACSSC.2016.7869163
DO - 10.1109/ACSSC.2016.7869163
M3 - Conference contribution
AN - SCOPUS:85016248707
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 827
EP - 830
BT - Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
A2 - Matthews, Michael B.
PB - IEEE Computer Society
T2 - 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
Y2 - 6 November 2016 through 9 November 2016
ER -