The Effect of Spoken Language on Speech Enhancement using Self-Supervised Speech Representation Loss Functions

• URL: http://arxiv.org/abs/2307.14502v2
• Date: Fri, 20 Oct 2023 08:55:17 GMT
• Title: The Effect of Spoken Language on Speech Enhancement using Self-Supervised Speech Representation Loss Functions
• Authors: George Close, Thomas Hain and Stefan Goetze
• Abstract summary: This work looks at the relationship between the language of the audio used to train self-supervised representation and that used to train the SE system. Enhancement models trained using a loss function which incorporates a self-supervised representation that shares exactly the language of the noisy data used to train the SE system show better performance than those which do not match exactly. It is found that the training language of the self-supervised representation appears to have a minor effect on enhancement performance.
• Score: 21.237026538221404
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent work in the field of speech enhancement (SE) has involved the use of self-supervised speech representations (SSSRs) as feature transformations in loss functions. However, in prior work, very little attention has been paid to the relationship between the language of the audio used to train the self-supervised representation and that used to train the SE system. Enhancement models trained using a loss function which incorporates a self-supervised representation that shares exactly the language of the noisy data used to train the SE system show better performance than those which do not match exactly. This may lead to enhancement systems which are language specific and as such do not generalise well to unseen languages, unlike models trained using traditional spectrogram or time domain loss functions. In this work, SE models are trained and tested on a number of different languages, with self-supervised representations which themselves are trained using different language combinations and with differing network structures as loss function representations. These models are then tested across unseen languages and their performances are analysed. It is found that the training language of the self-supervised representation appears to have a minor effect on enhancement performance, the amount of training data of a particular language, however, greatly affects performance.

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