Data-Driven Non-Intrusive Speech Intelligibility Prediction Using Speech Presence Probability-Reference-Cited by-同舟云学术

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Data-Driven Non-Intrusive Speech Intelligibility Prediction Using Speech Presence Probability

Published:2024 Issue: Volume:32 Page:55-67
ISSN:2329-9290
Container-title:IEEE/ACM Transactions on Audio, Speech, and Language Processing
language:
Short-container-title:IEEE/ACM Trans. Audio Speech Lang. Process.

Author:

Pedersen Mathias Bach¹^ORCID,Jensen Søren Holdt²^ORCID,Tan Zheng-Hua¹^ORCID,Jensen Jesper¹^ORCID

Affiliation:

1. Department of Electronic Systems, Aalborg University, Aalborg, Denmark

2. Danish Ministry of Defence Estate Agency, Hjørring, Denmark

Funder

Independent Research Fund Denmark

Intelligibility-Aware Hearing Assistive Devices Intelligibility Prediction

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Acoustics and Ultrasonics,Computer Science (miscellaneous),Computational Mathematics

Link

http://xplorestaging.ieee.org/ielx7/6570655/10304349/10271546.pdf?arnumber=10271546

Reference50 articles.

1. DNN-Based Speech Presence Probability Estimation for Multi-Frame Single-Microphone Speech Enhancement

2. Speech Enhancement Based on Teacher–Student Deep Learning Using Improved Speech Presence Probability for Noise-Robust Speech Recognition

3. STOI-Net: A deep learning based non-intrusive speech intelligibility assessment model;zezario;Proc IEEE Asia-Pacific Signal Inf Process Assoc Annu Summit Conf,0

4. A non-intrusive Short-Time Objective Intelligibility measure

5. Twin-HMM-based non-intrusive speech intelligibility prediction

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