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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.00028 |
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| _version_ | 1866909410087927808 |
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| author | Darwesh, Yaser Wern, Lit Wei Mustafa, Mumtaz Begum |
| author_facet | Darwesh, Yaser Wern, Lit Wei Mustafa, Mumtaz Begum |
| contents | Many of the existing TTS systems cannot accurately synthesize text containing a variety of numerical formats, resulting in reduced intelligibility of the synthesized speech. This research aims to develop a numerical format classifier that can classify six types of numeric contexts. Experiments were carried out using the proposed context-based feature extraction technique, which is focused on extracting keywords, punctuation marks, and symbols as the features of the numbers. Support Vector Machine, K-Nearest Neighbors Linear Discriminant Analysis, and Decision Tree were used as classifiers. We have used the 10-fold cross-validation technique to determine the classification accuracy in terms of recall and precision. It can be found that the proposed solution is better than the existing feature extraction technique with improvement to the classification accuracy by 30% to 37%. The use of the number format classification can increase the intelligibility of the TTS systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_00028 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Context-Based Numerical Format Prediction for a Text-To-Speech System Darwesh, Yaser Wern, Lit Wei Mustafa, Mumtaz Begum Audio and Speech Processing Machine Learning Many of the existing TTS systems cannot accurately synthesize text containing a variety of numerical formats, resulting in reduced intelligibility of the synthesized speech. This research aims to develop a numerical format classifier that can classify six types of numeric contexts. Experiments were carried out using the proposed context-based feature extraction technique, which is focused on extracting keywords, punctuation marks, and symbols as the features of the numbers. Support Vector Machine, K-Nearest Neighbors Linear Discriminant Analysis, and Decision Tree were used as classifiers. We have used the 10-fold cross-validation technique to determine the classification accuracy in terms of recall and precision. It can be found that the proposed solution is better than the existing feature extraction technique with improvement to the classification accuracy by 30% to 37%. The use of the number format classification can increase the intelligibility of the TTS systems. |
| title | A Context-Based Numerical Format Prediction for a Text-To-Speech System |
| topic | Audio and Speech Processing Machine Learning |
| url | https://arxiv.org/abs/2412.00028 |