Nonintrusive Speech Intelligibility Prediction Using Convolutional Neural Networks

Asger Heidemann Andersen, Jan Mark De Haan, Zheng-Hua Tan, Jesper Jensen

Research output: Contribution to journalJournal articleResearchpeer-review

9 Citations (Scopus)
113 Downloads (Pure)

Abstract

Speech Intelligibility Prediction (SIP) algorithms are becoming popular tools within the development and operation of speech processing devices and algorithms. However, many SIP algorithms require knowledge of the underlying clean speech; a signal that is often not available in real-world applications. This has led to increased interest in nonintrusive SIP algorithms, which do not require clean speech to make predictions. In this paper, we investigate the use of Convolutional Neural Networks (CNNs) for nonintrusive SIP. To do so, we utilize a CNN architecture that shows similarities to existing SIP algorithms, in terms of computational structure, and which allows for easy and meaningful visualization and interpretation of trained weights. We evaluate this architecture using a large dataset obtained by combining datasets from the literature. The proposed method shows high prediction performance when compared with four existing intrusive and nonintrusive SIP algorithms. This demonstrates the potential of deep learning for speech intelligibility prediction.

Original languageEnglish
JournalIEEE/ACM Transactions on Audio, Speech, and Language Processing
Volume26
Issue number10
Pages (from-to)1925-1939
Number of pages15
ISSN2329-9290
DOIs
Publication statusPublished - Oct 2018

Keywords

  • Nonintrusive speech intelligibility prediction
  • convolutional neural networks

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