AfriSwitch is a 61.36‑hour, human‑transcribed benchmark of in‑the‑wild code‑switched speech covering 16 African languages and varieties, annotated with switch‑level English span tags, per‑utterance Code‑Mixing Index (CMI), and switch‑point counts. The corpus reveals that code‑switching behaviour varies widely across languages, with no single metric fully capturing how code‑switched a language is. Benchmarking five open and commercial multilingual ASR systems in a zero‑shot setting shows high word error rates, with the best system averaging 35.93% WER and none dropping below 24% on any language, indicating that Africa‑targeted training rather than model scale or nominal language coverage best predicts performance.
arXiv:2607. 04814v1 Announce Type: cross Abstract: Extending automatic speech recognition (ASR) to low-resource African languages is constrained by the prohibitive demands of data collection at scale.
We present DONDO, a family of open, permissively licensed automatic speech recognition (ASR) base models for African languages, built on the w2v-BERT 2. 0 self-supervised speech encoder.
arXiv:2608.26697v1 Announce Type: new Abstract: Synthetic speech provides scalable supervision for automatic speech recognition (ASR), but its benefit depends on the selected texts, reference speech,...
arXiv:2606. 03219v1 Announce Type: cross Abstract: African languages have very little labelled data, and it is unclear if augmenting the quantity of annotation data reliably enhances downstream performance.
arXiv:2608. 12327v1 Announce Type: cross Abstract: Multilingual pretrained models nominally support Nepali, yet no controlled benchmark has compared them under a single fine-tuning protocol.
arXiv:2607. 23808v1 Announce Type: cross Abstract: In this work, we introduce Indic DiarBench, a speaker diarization and ASR benchmark dataset spanning all 22 scheduled languages of India.
arXiv:2607. 17164v1 Announce Type: new Abstract: Developing Automatic Speech Recognition (ASR) for morphologically rich, low-resource languages such as Assamese is challenging due to insufficient annotated speech data.
arXiv:2606. 19381v1 Announce Type: cross Abstract: Code-switch (CS) Automatic Speech Recognition (ASR) remains challenging due to limited availability of high quality CS text-speech pairs for training.
arXiv:2603. 29042v2 Announce Type: replace-cross Abstract: Phone recognition (PR) is a key enabler of multilingual and low-resource speech processing tasks, yet robust performance remains elusive.
arXiv:2608.26060v1 Announce Type: new Abstract: Automatic Speech Recognition (ASR) technologies have achieved remarkable performance in recent years through the use of large multilingual foundation m...
arXiv:2606. 12708v1 Announce Type: cross Abstract: Despite their linguistic diversity and global significance, African languages remain underrepresented in research and resources to support NLP.
arXiv:2608. 04586v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have achieved significant success in speech-to-text translation (S2TT).