Accent labeling algorithm based on morphological rules and machine learning in English conversion system
The dependency of a speech recognition system on the accent of a user leads to the variation in its performance, as the people from different backgrounds have different accents. Accent labeling and conversion have been reported as a prospective solution for the challenges faced in language learning...
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De Gruyter
2021
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oai:doaj.org-article:bf6d7f516c71462e9b3f88b5fadbf7ba2021-12-05T14:10:51ZAccent labeling algorithm based on morphological rules and machine learning in English conversion system2191-026X10.1515/jisys-2020-0144https://doaj.org/article/bf6d7f516c71462e9b3f88b5fadbf7ba2021-07-01T00:00:00Zhttps://doi.org/10.1515/jisys-2020-0144https://doaj.org/toc/2191-026XThe dependency of a speech recognition system on the accent of a user leads to the variation in its performance, as the people from different backgrounds have different accents. Accent labeling and conversion have been reported as a prospective solution for the challenges faced in language learning and various other voice-based advents. In the English TTS system, the accent labeling of unregistered words is another very important link besides the phonetic conversion. Since the importance of the primary stress is much greater than that of the secondary stress, and the primary stress is easier to call than the secondary stress, the labeling of the primary stress is separated from the secondary stress. In this work, the labeling of primary accents uses a labeling algorithm that combines morphological rules and machine learning; the labeling of secondary accents is done entirely through machine learning algorithms. After 10 rounds of cross-validation, the average tagging accuracy rate of primary stress was 94%, the average tagging accuracy rate of secondary stress was 94%, and the total tagging accuracy rate was 83.6%. This perceptual study separates the labeling of primary and secondary accents providing the promising outcomes.Liu XiaofengSingh Pradeep KumarPavlovich Pljonkin AntonDe Gruyterarticletext-to-speech conversionunregistered wordsstress annotationaccent labelingmachine learningScienceQElectronic computers. Computer scienceQA75.5-76.95ENJournal of Intelligent Systems, Vol 30, Iss 1, Pp 881-892 (2021) |
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DOAJ |
| collection |
DOAJ |
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EN |
| topic |
text-to-speech conversion unregistered words stress annotation accent labeling machine learning Science Q Electronic computers. Computer science QA75.5-76.95 |
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text-to-speech conversion unregistered words stress annotation accent labeling machine learning Science Q Electronic computers. Computer science QA75.5-76.95 Liu Xiaofeng Singh Pradeep Kumar Pavlovich Pljonkin Anton Accent labeling algorithm based on morphological rules and machine learning in English conversion system |
| description |
The dependency of a speech recognition system on the accent of a user leads to the variation in its performance, as the people from different backgrounds have different accents. Accent labeling and conversion have been reported as a prospective solution for the challenges faced in language learning and various other voice-based advents. In the English TTS system, the accent labeling of unregistered words is another very important link besides the phonetic conversion. Since the importance of the primary stress is much greater than that of the secondary stress, and the primary stress is easier to call than the secondary stress, the labeling of the primary stress is separated from the secondary stress. In this work, the labeling of primary accents uses a labeling algorithm that combines morphological rules and machine learning; the labeling of secondary accents is done entirely through machine learning algorithms. After 10 rounds of cross-validation, the average tagging accuracy rate of primary stress was 94%, the average tagging accuracy rate of secondary stress was 94%, and the total tagging accuracy rate was 83.6%. This perceptual study separates the labeling of primary and secondary accents providing the promising outcomes. |
| format |
article |
| author |
Liu Xiaofeng Singh Pradeep Kumar Pavlovich Pljonkin Anton |
| author_facet |
Liu Xiaofeng Singh Pradeep Kumar Pavlovich Pljonkin Anton |
| author_sort |
Liu Xiaofeng |
| title |
Accent labeling algorithm based on morphological rules and machine learning in English conversion system |
| title_short |
Accent labeling algorithm based on morphological rules and machine learning in English conversion system |
| title_full |
Accent labeling algorithm based on morphological rules and machine learning in English conversion system |
| title_fullStr |
Accent labeling algorithm based on morphological rules and machine learning in English conversion system |
| title_full_unstemmed |
Accent labeling algorithm based on morphological rules and machine learning in English conversion system |
| title_sort |
accent labeling algorithm based on morphological rules and machine learning in english conversion system |
| publisher |
De Gruyter |
| publishDate |
2021 |
| url |
https://doaj.org/article/bf6d7f516c71462e9b3f88b5fadbf7ba |
| work_keys_str_mv |
AT liuxiaofeng accentlabelingalgorithmbasedonmorphologicalrulesandmachinelearninginenglishconversionsystem AT singhpradeepkumar accentlabelingalgorithmbasedonmorphologicalrulesandmachinelearninginenglishconversionsystem AT pavlovichpljonkinanton accentlabelingalgorithmbasedonmorphologicalrulesandmachinelearninginenglishconversionsystem |
| _version_ |
1718371687699841024 |