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Building African Voices

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abstract

Modern speech synthesis techniques can produce natural-sounding speech given sufficient high-quality data and compute resources. However, such data is not readily available for many languages. This paper focuses on speech synthesis for low-resourced African languages, from corpus creation to sharing and deploying the Text-to-Speech (TTS) systems. We first create a set of general-purpose instructions on building speech synthesis systems with minimum technological resources and subject-matter expertise. Next, we create new datasets and curate datasets from "found" data (existing recordings) through a participatory approach while considering accessibility, quality, and breadth. We demonstrate that we can develop synthesizers that generate intelligible speech with 25 minutes of created speech, even when recorded in suboptimal environments. Finally, we release the speech data, code, and trained voices for 12 African languages to support researchers and developers.

fields

cs.CL 1

years

2025 1

verdicts

CONDITIONAL 1

representative citing papers

Natural language processing for African languages

cs.CL · 2025-06-30 · conditional · novelty 3.0

A doctoral dissertation that consolidates previously published African-language NLP contributions, including the AfroXLMR model and MasakhaNER datasets for 21 languages.

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  • Natural language processing for African languages cs.CL · 2025-06-30 · conditional · none · ref 7032 · internal anchor

    A doctoral dissertation that consolidates previously published African-language NLP contributions, including the AfroXLMR model and MasakhaNER datasets for 21 languages.