Author:
Brown Georgina,Franco-Pedroso Javier,González-Rodríguez Joaquin
Abstract
Traditionally, work in automatic accent recognition has followed a similar research trajectory to that of language identification, dialect identification and automatic speaker recognition. The same acoustic modelling approaches that have been implemented in speaker recognition (such as GMM-UBM and i-vector-based systems) have also been applied to automatic accent recognition. These approaches form models of speakers’ accents by taking acoustic features from right across the speech signal without knowledge of its phonetic content. Particularly for accent recognition, however, phonetic information is expected to add substantial value to the task. The current work presents an alternative modelling approach to automatic accent recognition, which forms models of speakers’ pronunciation systems using segmental information. This article claims that such an approach to the problem makes for a more explainable method and therefore is a more appropriate method to deploy in settings where it is important to be able to communicate methods, such as forensic applications. We discuss the issue of explainability and show how the system operates on a large 700-speaker dataset of non-native English conversational telephone recordings.
Subject
Law,Linguistics and Language
Reference50 articles.
1. Adadi, A. and Berrada, M. Peeking inside the black-box: a survey on Explainable Artifical Intelligence (XAI). IEEE Access. 6. 52138-52160.
2. D’Arcy, S., Russell, M., Browning, S. and Tomlinson, M. (2004). The Accents of the British Isles (ABI) corpus. In Proceedings of Modélisations pour l’Identification des Langues. Paris, France. 115-119.
3. Bahari, M.H., Saeidi, R., Van Hamme, H., Van Leeuwen, D. (2013). Accent recognition using i-vector, gaussian mean supervector and gaussian posterior probability supervector for spontaneous telephone speech. In Proceedings of the International Conference on Acoustics, Speech and Signal Processing. Vancouver, Canada. 7344-7348.
4. Behravan, H. Hautamäki, V. and Kinnunen, T. (2013). Foreign accent detection from spoken Finnish using i-vectors. In Proceedings of Interspeech. Lyon, France. 79-82.
5. Behravan, H., Hautamäki, V. and Kinnunen, T. (2015). Factors affecting i-vector based foreign accent recognition: A case study in spoken Finnish. Speech Communication. 66. 118-129.
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献