Bridging Swahili Communication Gaps: Real-Time Audio-to-Text Sentiment Analysis via Pre-trained NLP

The global proliferation of digital communication highlights a critical gap in language technologies for digitally under-represented languages, particularly Kiswahili, a language spoken by over 100 million people. While significant advancements have been made in natural language processing (NLP) for high-resource languages like English, a persistent challenge remains in creating robust computational systems for low-resource linguistic contexts. This study addresses this challenge by presenting a novel, end-to-end Kiswahili audio processing pipeline that unifies three core capabilities; real-time speech recognition, sentiment analysis, and text summarization. The system’s novelty lies in its strategic leverage of state-of-the-art, pre-trained machine learning models, including Wav2vec2, DistilBERT, and T5, demonstrating a viable approach to bridging the digital communication gap for Kiswahili in real-world applications. Our methodology involved a rigorous evaluation of the integrated system using the Mozilla Common Voice Corpus. The results revealed key insights and promising performance metrics. The speech recognition component, a foundational element of the pipeline, achieved an exceptionally low Word Error Rate (WER) of 0.3329 with the Wav2vec2 model, highlighting its capacity for accurate transcription in a low-resource setting. This is a significant finding, as it suggests that models specifically fine-tuned for such environments can overcome the challenges of data scarcity and linguistic diversity. The summarization component also demonstrated strong capabilities, yielding a ROUGE-L score of 0.6622, which indicates robust semantic and structural alignment with reference texts. While the sentiment analysis revealed a notable data imbalance with a predominance of negative samples, the model achieved a 60% accuracy, demonstrating its potential for further refinement. These findings underscore both the immense potential and the inherent limitations of applying pre-trained models to a low-resource language like Kiswahili. They provide a compelling proof of concept for the technical feasibility of Kiswahili audio processing and emphasize the critical need for continued investment in dataset expansion and model optimization. The study concludes that this work establishes a foundational groundwork for continued research and the subsequent development of advanced NLP tools specifically tailored for Kiswahili-speaking populations, ultimately aiming to improve access to education, healthcare, and information services, and to foster greater digital inclusion throughout East Africa.