BENYO-S2ST-Corpus-1: A Bilingual English-to-Yoruba Direct Speech-to-Speech Translation Corpus

TL;DR

This paper introduces BENYO-S2ST-Corpus-1, a large-scale bilingual speech dataset for English-to-Yoruba translation, created using a hybrid approach with AI-generated and augmented audio, enabling improved S2ST models for low-resource languages.

Contribution

The study presents a novel hybrid architecture for large-scale direct S2ST corpus creation, combining real and AI-generated data with augmentation techniques, specifically for English-Yoruba translation.

Findings

Created a 24,064 sample bilingual corpus with 41.20 hours of audio

Built a pretrained Yoruba TTS model with moderate pitch accuracy

Provided publicly available resources for low-resource language translation

Abstract

There is a major shortage of Speech-to-Speech Translation (S2ST) datasets for high resource-to-low resource language pairs such as English-to-Yoruba. Thus, in this study, we curated the Bilingual English-to-Yoruba Speech-to-Speech Translation Corpus Version 1 (BENYO-S2ST-Corpus-1). The corpus is based on a hybrid architecture we developed for large-scale direct S2ST corpus creation at reduced cost. To achieve this, we leveraged non speech-to-speech Standard Yoruba (SY) real-time audios and transcripts in the YORULECT Corpus as well as the corresponding Standard English (SE) transcripts. YORULECT Corpus is small scale(1,504) samples, and it does not have paired English audios. Therefore, we generated the SE audios using pre-trained AI models (i.e. Facebook MMS). We also developed an audio augmentation algorithm named AcoustAug based on three latent acoustic features to generate augmented audios from the raw audios of the two languages. BENYO-S2ST-Corpus-1 has 12,032 audio samples per language, which gives a total of 24,064 sample size. The total audio duration for the two languages is 41.20 hours. This size is quite significant. Beyond building S2ST models, BENYO-S2ST-Corpus-1 can be used to build pretrained models or improve existing ones. The created corpus and Coqui framework were used to build a pretrained Yoruba TTS model (named YoruTTS-1.5) as a proof of concept. The YoruTTS-1.5 gave a F0 RMSE value of 63.54 after 1,000 epochs, which indicates moderate fundamental pitch similarity with the reference real-time audio. Ultimately, the corpus architecture in this study can be leveraged by researchers and developers to curate datasets for multilingual high-resource-to-low-resource African languages. This will bridge the huge digital divides in translations among high and low-resource language pairs. BENYO-S2ST-Corpus-1 and YoruTTS-1.5 are publicly available at (https://bit.ly/40bGMwi).