Developing an audio search engine for Amharic speech web resources

Abstract While general-purpose search engines primarily serve English-language content, the web has seen enormous growth in non-resource-rich languages like Amharic. As a morphologically complex language with unique linguistic characteristics, Amharic presents significant challenges for information retrieval, particularly for speech content. Although Amharic web resources are expanding across text, speech, and video formats, speech retrieval demands specialized solutions due to three key challenges: (1) the absence of explicit word boundaries requiring accurate automatic speech recognition, (2) lack of visual context compared to video content, and (3) compounding effects of Amharic's rich morphology on transcription accuracy. These challenges are exacerbated by the proliferation of online radio broadcasts, speech reports, and news content in Amharic. This study presents a dedicated Audio Search Engine for Amharic speech web resources, addressing these challenges through four key innovations: (1) an enhanced web crawler optimized for Amharic speech content, (2) robust speech transcription pipelines, (3) efficient indexing of transcribed content, and (4) language-specific query preprocessing components. Our system leverages open-source technologies, including JSpider for crawling, Sphinx for speech recognition, and Datafari for indexing and retrieval, creating an integrated solution tailored to Amharic's linguistic characteristics. Evaluation results demonstrate the system's effectiveness, achieving 80% precision in top-10 results and 92% recall compared to baseline retrieval methods. These promising results highlight our solution's capability to handle Amharic's unique challenges while providing practical retrieval performance. The study contributes both a technical framework for Amharic speech search and insights applicable to other resource-constrained languages facing similar retrieval challenges.