The scarcity of high-quality, phonetically diverse datasets for low-resource languages like Amharic remains a significant barrier to high-fidelity speech synthesis. We present AmhTTS, the first open, natively evaluated Amharic text-to-speech system, developed by fine-tuning SpeechT5 on a curated 25-hour, single-speaker Amharic corpus paired with a HiFi-GAN vocoder. Unlike Meta MMS, the only other publicly available Amharic TTS system, which requires Latin script transliteration and reports no Amharic-specific evaluation, AmhTTS operates natively on Ethiopic script and is the first system to report human MOS scores on Amharic speech synthesis. To address the orthographic complexities of the Ethiopic script, we implement a three-stage text normalisation pipeline covering homophone unification, Ge’ez numeral expansion, and Amharic punctuation handling. System quality is assessed through a rigorous human evaluation via a purpose-built web platform, involving 28 native Amharic-speaking voluntary evaluators who rated 35 linguistically motivated samples across four domains: phonetic coverage (ejectives and labialized consonants), gemination and prosody, numeral and date reading, and conversational fluency. Results yield an overall naturalness MOS of 3.71 and intelligibility MOS of 4.02 on a 5-point scale, demonstrating that the rich pre-trained representations of SpeechT5 transfer effectively to a low-resource language with only 25 hours of data a fraction of the 460 hours used in the original English fine-tuning. While the system achieves high phonetic accuracy (MOS: 4.22/4.36), numeral synthesis and gemination modeling remain areas for future improvement. Given the demonstrated community demand for accessibility tools, we publicly release our fine-tuned model weights, the AmhTTS corpus, and evaluation data to advance low-resource TTS research. Our web application is currently active, serving users at multiple university disability centers in Ethiopia.

While Machine Translation (MT) research has progressed over the years, translation systems still suffer from biases, including gender bias. While an active line of research studies the existence and mitigation strategies of gender bias in machine translation systems, there is limited research exploring this phenomenon for low-resource languages. The limited availability of linguistic and computational resources confounded with the lack of benchmark datasets makes studying bias for low-resourced languages that much more difficult. In this paper, we construct benchmark datasets to evaluate gender bias in machine translation for three low-resource languages: Afaan Oromoo (Orm), Amharic (Amh), and Tigrinya (Tir). Building on prior work, we collected 2400 gender-balanced sentences parallelly translated into the three languages. From human evaluations of the dataset we collected, we found that about 93% of Afaan Oromoo, 80% of Tigrinya, and 72% of Amharic sentences exhibited gender bias. In addition to providing benchmarks for improving gender bias mitigation research in the three languages, we hope the careful documentation of our work will help other low-resourced language researchers extend our approach to their languages.

Large language models (LLMs) have received a lot of attention in natural language processing (NLP) research because of their exceptional performance in understanding and generating human languages. However, low-resource languages are left behind due to the unavailability of resources. In this work, we focus on enhancing the LLaMA-2-Amharic model by integrating task-specific and generative datasets to improve language model performance for Amharic. We compile an Amharic instruction fine-tuning dataset and fine-tuned LLaMA-2-Amharic model. The fine-tuned model shows promising results in different NLP tasks. We also explore the effectiveness of translated instruction datasets compared to the dataset we created. Our dataset creation pipeline, along with instruction datasets, trained models, and evaluation outputs, is made publicly available to encourage research in language-specific models.