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Tone modelling in Ibibio speech synthesis

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Abstract

In this paper, we investigate the contribution of tone in a Hidden Markov Model (HMM)-based speech synthesis of Ibibio (ISO 693-3: nic; Ethnologue: IBB), an under-resourced language. We review the language’s speech characteristics, required for building the front end components of the design and propose a finite state transducer (FST), useful for modelling the language’s tonetactics. The existing speech database of Ibibio is also studied and the quality of synthetic speech examined through a spectral analysis of voices obtained from two synthesis experiments, with and without tone feature labels. A confusion matrix classifying the results of a controlled listening test for both experiments is constructed and statistics comparing their performance quality presented. Results obtained revealed that synthesis systems with tone feature labels outperformed synthesis systems without tone feature labels, as more tone confusions were perceived by listeners in the latter.

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Notes

  1. The [u] sound, however occurs in word finally in very few words based on dialectal (free) variation. Some dialects of Ibibio have the word, rat as ‘’ while others have it as ‘’. This is just one exception in the distribution of Ibibio central vowels.

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Acknowledgements

We appreciate Dr. Okokon Akpan, a lecturer in the Department of Linguistics and Nigerian Languages, University of Uyo, for responding to our request—to record and use his voice for the Ibibio speech database. We also thank the anonymous reviewers for their excellent comments.

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Authors and Affiliations

  1. Department of Computer Science, University of Uyo, P.M.B. 1017, 520003, Uyo, Akwa Ibom State, Nigeria

    Moses E. Ekpenyong

  2. Department of Linguistics and Nigerian Languages, University of Uyo, P.M.B. 1017, 520003, Uyo, Akwa Ibom State, Nigeria

    EmemObong Udoh

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  1. Moses E. Ekpenyong
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  2. EmemObong Udoh
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Correspondence to Moses E. Ekpenyong.

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Ekpenyong, M.E., Udoh, E. Tone modelling in Ibibio speech synthesis. Int J Speech Technol 17, 145–159 (2014). https://doi.org/10.1007/s10772-013-9216-2

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