Newest papers

AfriBED: a Speech Recognition Bias Evaluation Dataset for Low Resource African Languages

Claytone Sikasote, Hussein Suleman, Jan Buys · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26821 · cited by 0 works (OpenAlex) · open-access PDF

Spanish

A pesar de los avances en los sistemas de reconocimiento automático del habla (ASR), los sistemas actuales exhiben bias del hablante-atributo, es decir, su rendimiento del reconocimiento del habla está bias contra determinados hablantes en función de sus atributos demográficos como género, acentos y tipo de hablante. Sin embargo, evaluar con precisión el bias del atributo del hablante en un sistema ASR dado requiere la disponibilidad de un conjunto de datos de evaluación del bias con habla comparable entre subgrupos de un atributo del hablante. En este artículo, presentamos el Dataset de Evaluación del Bias del habla africano (AfriBED), un conjunto de datos de evaluación del bias del reconocimiento del habla para dos idiomas africanos de bajo recurso, Bemba

Machine translation (Meta M2M100); the English original follows.

Abstract

Despite advancements in automatic speech recognition systems (ASR), current systems exhibit speaker-attribute bias, i.e., their speech recognition performance is biased against certain speakers based on their demographic attributes such as gender, accents, and speaker type. However, accurately assessing speaker attribute bias in a given ASR system requires the availability of a bias evaluation dataset with comparable speech across subgroups of a speaker attribute. In this paper, we introduce African speech Bias Evaluation Dataset (AfriBED), a novel speech recognition bias evaluation dataset for two low resource African languages, Bemba and Nyanja, to detect and estimate gender and speaker-type bias for ASR systems. The dataset comprises 9 hours of speech with 3928 utterances recorded by 80 speakers for Bemba, while the Nyanja set includes 6260 utterances with approximately 18 hours of speech recorded by 127 speakers. The results of our baseline experiments demonstrate that the datasets are useful for examining gender and speaker-type attribute bias in the target languages.

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Topics: Computer science, Speech recognition, Set (abstract data type), Speaker recognition, Voice activity detection, Speech processing

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