| Autor: |
SANI, YAHAYA MOHAMMED, OLUYEMI ETUK, STELLA, ANDA, ILYASU, ADAMU, MAMMAN |
| Předmět: |
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| Zdroj: |
i-Manager's Journal of Pattern Recognition; Sep-Nov2018, Vol. 5 Issue 3, p29-36, 8p |
| Abstrakt: |
This paper presents a framework for a text-to-speech translation on Android Devices based on Natural Language Processing (NLP) and text-to-speech synthesizer (TTS) to deliver real-time agricultural update to farmers by agricultural extension service workers (AEW) as speech is the most used and natural way for people to communicate with one another. In order to increase the naturalness of oral communications between Agricultural Extension Service workers and farmers, speech aspects must be involved. This is because most local farmers have good understanding of their local language and have strong preferences for it over any other language. Since, majority of farmers are in rural areas, they have little or no understanding of English language, and when agriculture research output is communicated in English language, it may be of little or no use to them, if delivered in a foreign language. Text-to-Speech Enabled Hybrid Multilingual Translation framework adopts a serial integration of NLP on one hand and TTS interpretation technique using android google translate API text-to-speech synthesizer and recognizer to translate English, Hausa, Yoruba, Ibo, and Arabic texts in to speech(es), respectively in accordance with farmers registered dialect on the other hand. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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