An examination of speech analytics technologies in processing Yoruba news broadcasts in Ibadan
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Background of the Study
Speech analytics technologies have revolutionized the processing of spoken language data, enabling the automated analysis of broadcast media. In Ibadan, Yoruba news broadcasts are an essential medium for information dissemination, reflecting local culture and societal issues. The integration of speech recognition, natural language processing, and sentiment analysis into broadcast media has enabled real-time transcription, content classification, and audience engagement analysis. Researchers (Adewale, 2023) note that these technologies help broadcasters monitor and improve the quality of news delivery. However, Yoruba’s tonal and rhythmic features, as well as its rich idiomatic expressions, present significant challenges for automated speech analytics. Recent advances in acoustic modeling and deep learning algorithms have shown promise in addressing these challenges (Ogunleye, 2024). Furthermore, improved computational frameworks have enabled more precise segmentation and analysis of broadcast speech. Despite these advancements, inconsistencies in audio quality and dialectal variations remain obstacles. This study examines current speech analytics technologies applied to Yoruba news broadcasts in Ibadan, aiming to evaluate their accuracy and identify areas for further technological refinement to support effective media processing.
Statement of the Problem
Despite advancements in speech analytics, current technologies struggle with accurately processing Yoruba news broadcasts in Ibadan due to the language’s tonal complexity and dialectal variations (Adewale, 2023). Inconsistent audio quality and rapid speech delivery further hinder transcription accuracy, resulting in misinterpretations and reduced reliability of automated analyses (Ogunleye, 2024). These challenges limit the effectiveness of speech analytics in content moderation, audience analysis, and media quality control. Moreover, insufficient adaptation of existing models to Yoruba’s phonetic nuances exacerbates these issues. A systematic examination of current speech analytics technologies is needed to identify their limitations and propose improvements that enhance processing accuracy and reliability in real-world broadcast settings.
Objectives of the Study
- To assess the performance of speech analytics technologies on Yoruba news broadcasts in Ibadan.
- To identify specific challenges related to tonal and dialectal variations in Yoruba.
- To recommend technological enhancements for improved speech processing accuracy.
Research Questions
- How effective are current speech analytics tools in transcribing Yoruba news broadcasts?
- What challenges do tonal and dialectal variations pose to these technologies?
- How can speech analytics systems be refined to better handle Yoruba’s unique features?
Significance of the Study
This study is significant because it evaluates the application of speech analytics technologies in processing Yoruba news broadcasts, providing insights that can enhance media quality and audience engagement in Ibadan. Improved speech processing will benefit broadcasters, researchers, and policymakers by enabling more accurate content analysis and real-time monitoring. The findings will contribute to the development of culturally adapted technologies, promoting better communication and information dissemination in local media.
Scope and Limitations of the Study
This study is limited to speech analytics technologies for Yoruba news broadcasts in Ibadan. It does not extend to other media formats or languages.
Definitions of Terms
- Speech Analytics: The automated process of analyzing spoken language data using computational methods.
- News Broadcasts: Audio or video transmissions that disseminate current events and information.
- Yoruba: An indigenous language spoken widely in southwestern Nigeria.
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