Research Article | OPEN ACCESS
Statistical Parametric Speech Synthesis of Malay Language using Found Training Data
Lau Chee Yong and Tan Tian Swee
Medical Implant Technology Group (MediTEG), Cardiovascular Engineering Center, Material Manufacturing Research Alliance (MMRA), Faculty of Biosciences and Medical Engineering (FBME), Universiti Teknologi Malaysia, Malaysia
Research Journal of Applied Sciences, Engineering and Technology 2014 24:5143-5147
Received: January 28, 2014 | Accepted: February 10, 2014 | Published: June 25, 2014
Abstract
The preparation of training data for statistical parametric speech synthesis can be sophisticated. To ensure the good quality of synthetic speech, high quality low noise recording must be prepared. The preparation of recording script can be also tremendous from words collection, words selection and sentences design. It requires tremendous human effort and takes a lot of time. In this study, we used alternative free source of recording and text such as audio-book, clean speech and so on as the training data. Some of the free source can provide high quality recording with low noise which is suitable to become training data. Statistical parametric speech synthesis method applying Hidden Markov Model (HMM) has been used. To test the reliability of synthetic speech, perceptual test has been conducted. The result of naturalness test is fairly reasonable. The intelligibility test showed encouraging result. The Word Error Rate (WER) for normal synthetic sentences is below 15% while for Semantically Unpredictable Sentences (SUS) is averagely in 30%. In short, using free and ready source as training data can leverage the process of preparing training data while obtaining motivating synthetic result.
Keywords:
Hidden Markov Model (HMM), letter to sound rule, statistical parametric speech synthesis,
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Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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