2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
Login Paper Search My Schedule Paper Index Help

My ICASSP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDAUD-11.6
Paper Title AN END-TO-END NON-INTRUSIVE MODEL FOR SUBJECTIVE AND OBJECTIVE REAL-WORLD SPEECH ASSESSMENT USING A MULTI-TASK FRAMEWORK
Authors Zhuohuang Zhang, Piyush Vyas, Xuan Dong, Donald S. Williamson, Indiana University, United States
SessionAUD-11: Auditory Modeling and Hearing Instruments
LocationGather.Town
Session Time:Wednesday, 09 June, 14:00 - 14:45
Presentation Time:Wednesday, 09 June, 14:00 - 14:45
Presentation Poster
Topic Audio and Acoustic Signal Processing: [AUD-QIM] Quality and Intelligibility Measures
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
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.