Audio speech enhancement using masks derived from visual speech

Websdale, Danny (2018) Audio speech enhancement using masks derived from visual speech. Doctoral thesis, University of East Anglia .

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Abstract

The aim of the work in this thesis is to explore how visual speech can be used within monaural masking based speech enhancement to remove interfering noise, with a focus on improving intelligibility. Visual speech has the advantage of not being corrupted by interfering noise and can therefore provide additional information within a speech enhancement framework. More specifically, this work considers
audio-only, visual-only and audio-visual methods of mask estimation within deep learning architectures with application to both seen and unseen noise types.

To estimate masks from audio and visual speech information, models are developed using deep neural networks, specifically feed-forward (DNN) and recurrent (RNN) neural networks for temporal modelling and convolutional neural networks (CNN) for visual feature extraction. It was found that the proposed layer normalised bi-directional feed-forward hybrid network using gated recurrent units (LNBiGRUDNN) provided best performance across all objective measures for temporal modelling. Also, extracting visual features using both pre-trained and end-to-end trained CNNs outperform traditional active appearance model (AAM) feature extraction across all noise types and SNRs tested. End-to-end CNNs trained on images focused on mouth-only regions-of-interest provided best performance for both audio-visual and visual-only models.

The best performing audio-visual masking method outperformed both audio-only and visual-only masking methods in both matched and unseen noise type and SNR dependent conditions. For example, in unseen cafeteria babble noise at -10 dB, audio-visual masking had an ESTOI of 46.8, while audio-only and visual-only masking scored 15.0 and 42.4, and the unprocessed audio scored 9.3. Formal tests show that visual information is critical for improving intelligibility at low SNRs and for generalisation to unseen noise conditions. Experiments in large unconstrained vocabulary speech confirm that the model architectures and approaches developed can generalise to unconstrained speech across noise independent conditions and can be considered for monaural speaker dependent real-world applications.

Item Type: Thesis (Doctoral)
Faculty \ School: Faculty of Science > School of Computing Sciences
Depositing User: Zoe White
Date Deposited: 19 Jun 2019 11:16
Last Modified: 19 Jun 2019 11:16
URI: https://ueaeprints.uea.ac.uk/id/eprint/71488
DOI:

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