Syllable Segmentation with Vowel Detection on Verse Quranic Recitation

In speech recognition, segmentation involves partitioning a continuous audio signal containing speech into smaller units or segments, such as words, phonemes, or syllables. This process is paramount in speech recognition systems, as it delineates the boundaries between distinct speech elements, facilitating subsequent analysis and processing. Segmentation accuracy significantly impacts speech recognition systems' overall precision and performance, enabling more precise identification and processing of individual speech units. Moreover, proper segmentation empowers the automatic speech recognition (ASR) system to distinguish between different syllables or words effectively, leading to more efficient speech recognition outcomes. This research paper investigates the importance of vowel detection for syllable segmentation in speech recognition, particularly in Arabic speech, such as the Quran, where changes in each syllable can alter the meaning. While existing techniques only consider pronunciation by different readers, this study employs onset detection to account for the presence of Arabic vowels. Specifically, the study focuses on detecting the onset of the recitation of Surah Al-Fatihah's fourth verse using 50 data sets in the syllable detection testing process. The results indicate that syllable detection performs excellently on syllables with /a/ and /i/ vowels. However, syllables with /u/ vowels produce results below 70%. The study suggests that the onset-based method is ideal for syllables with the presence of /a/and /i/ vowels, demonstrating the importance of considering Arabic vowel letters in speech recognition.