Jochebed Afua Basil
Author directory2026
MGhana-ST: A Low-Resource Speech Translation Dataset for Ghanaian Languages and an Analysis of Multilingual Training Trade-offs
Frank Lawrence Nii Adoquaye Acquaye | Eric George Parakal | Jesse Johnson | Kishankumar Bhimani | Jochebed Afua Basil
Proceedings of the 6th Workshop on Multilingual Representation Learning (MRL 2026)
Frank Lawrence Nii Adoquaye Acquaye | Eric George Parakal | Jesse Johnson | Kishankumar Bhimani | Jochebed Afua Basil
Proceedings of the 6th Workshop on Multilingual Representation Learning (MRL 2026)
We present MGhana-ST, a speech translation dataset for four low-resource Ghanaian language varieties: Ga, Twi (Akuapem and Asante), Ewe, and Fante. MGhana-ST is an ongoing annotation effort; the experiments reported here use a fixed subset of approximately 16.1 hours of paired speech and English translation data. The audio is curated from two existing Ghanaian speech resources. The English translations in MGhana-ST are produced directly from the audio by 37 native-speaker annotators, rather than derived from source-language transcriptions as in the upstream resources, and are accompanied by verbal and non-verbal event annotations.
Using Whisper-small as a backbone, we examine monolingual and multilingual training under severe data scarcity, reporting all baseline results as means over three training seeds. We find that flat multilingual training benefits no variety in this regime. Ga and Twi are unchanged within seed variance (+0.51 and +0.06 BLEU against monolingual standard deviations of 1.63 and 2.20), while Ewe declines by 6.99 BLEU and Fante by 5.11. The two varieties that degrade are the typologically isolated one, which is also drawn from a different source corpus, and the least-resourced one. We further compare empirical cross-lingual transfer with typology-based similarity and find that, in this four-language setting, transfer BLEU identifies which language pairs interact more closely than URIEL similarity does, although neither measure predicts which varieties benefit from joint training.
We also report a methodological finding of independent interest. An earlier single-run version of this analysis found positive transfer for three of four varieties; that result did not survive replication across seeds. For Ga and Twi, monolingual baselines trained on two to six hours of audio have seed standard deviations of 1.63 and 2.20 BLEU, roughly five and thirty times those of the corresponding multilingual models (0.35 and 0.07). When the monolingual condition is the noisier one, a single-run comparison can show apparent transfer of this size from seed variation alone. We release MGhana-ST to support future research on African language speech technologies and low-resource multilingual speech translation.
Using Whisper-small as a backbone, we examine monolingual and multilingual training under severe data scarcity, reporting all baseline results as means over three training seeds. We find that flat multilingual training benefits no variety in this regime. Ga and Twi are unchanged within seed variance (+0.51 and +0.06 BLEU against monolingual standard deviations of 1.63 and 2.20), while Ewe declines by 6.99 BLEU and Fante by 5.11. The two varieties that degrade are the typologically isolated one, which is also drawn from a different source corpus, and the least-resourced one. We further compare empirical cross-lingual transfer with typology-based similarity and find that, in this four-language setting, transfer BLEU identifies which language pairs interact more closely than URIEL similarity does, although neither measure predicts which varieties benefit from joint training.
We also report a methodological finding of independent interest. An earlier single-run version of this analysis found positive transfer for three of four varieties; that result did not survive replication across seeds. For Ga and Twi, monolingual baselines trained on two to six hours of audio have seed standard deviations of 1.63 and 2.20 BLEU, roughly five and thirty times those of the corresponding multilingual models (0.35 and 0.07). When the monolingual condition is the noisier one, a single-run comparison can show apparent transfer of this size from seed variation alone. We release MGhana-ST to support future research on African language speech technologies and low-resource multilingual speech translation.