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AN END-TO-END NON-INTRUSIVE MODEL FOR SUBJECTIVE AND OBJECTIVE REAL-WORLD SPEECH ASSESSMENT USING A MULTI-TASK FRAMEWORK

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Citation Author(s):
Zhuohuang Zhang, Piyush Vyas, Xuan Dong, Donald S. Williamson
Submitted by:
Zhuohuang Zhang
Last updated:
22 June 2021 - 12:22pm
Document Type:
Poster
Document Year:
2021
Event:
Paper Code:
3995
Categories:

Abstract 

Abstract: 

Speech assessment is crucial for many applications, but current intrusive methods cannot be used in real environments. Data-driven approaches have been proposed, but they use simulated speech materials or only estimate objective scores. In this paper, we propose a novel multi-task non-intrusive approach that is capable of simultaneously estimating both subjective and objective scores of real-world speech, to help facilitate learning. This approach enhances our prior work, which estimated subjective mean-opinion scores, where our
approach now operates directly on the time-domain signal in an end-to-end fashion. The proposed system is compared against several state-of-the-art systems. The experimental results show that our multi-task and end-to-end framework leads to higher correlation performance and lower prediction errors, according to multiple evaluation measures.

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