arXiv:2606. 24169v1 Announce Type: new Abstract: Adapting a streaming speech recognition model to a new language requires choosing between two plausible warm starts: a multilingual (ML) encoder or an English-only (EN) encoder.
By Nenad Banfic
The paper introduces WaxalNet, a benchmark and adaptation study for spontaneous speech recognition across 19 African languages using the WAXAL corpus. It evaluates zero‑shot and fine‑tuned performance of several multilingual foundation models (Whisper, MMS, Omnilingual ASR, SeamlessM4T‑v2), showing that fine‑tuning can substantially reduce word error rates and that LLM decoders generally outperform CTC decoders when the encoder is shared. The authors also demonstrate that a single multilingual model can serve all languages with only a modest WER penalty and provide a native‑speaker audit of failure modes.
By Victor Tolulope Olufemi, Oreoluwa Babatunde, Ramsey Njema, Bolarinwa Gbotemi, Wanchi Lucia Yen, John Uzodinma, Sunday Ajayi, Oluwademilade Williams, Kausar Moshood, Innocent Elendu Anyaele, Akebert Arefaine, Candace Hunzwi, Wongel Dawit Daniel, Emmilly Namuganga, Cleophas Kadima, Athanase Bahizire, Onitsiky Ranaivoson, Emmanuel Aaron, Nicholaus Ladislaus, Idris Muhammed, Jonathan Enoch Simenya, Martin Koome, Matewos Tegete Endaylalu, Peter Ifeoluwa Adeyemo, Hondi Prisca Birindwa, Ukachi Agnes Eze-Mbey, Yacoba Oduro-Yeboah, Pericles Adjovi, Mikel K. Ngueajio, Toluwani Aremu, Prasenjit Mitra
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.
By Suman Paudel, Sarbin Sayami
The paper presents the Itgan systems for the NADI 2026 Arabic ASR shared task, covering robust country-level, mixed-dialect, and Tunisian code‑switched subtasks. All systems use a Whisper model adapted with LoRA on consumer GPUs, with task‑specific additions: per‑dialect specialists for 1.1, base‑model choice and decorrelated ensemble for 1.2, and a weight‑space average plus ROVER voting for 1.3. The submissions achieved 57.1% WER on 1.1, 46.7% WER on 1.2, and 14.49% WER on 1.3, with detailed statistical comparisons and noted ineffective approaches.
By Ibrahim Almajai
ufakzeka-kulak is an open Turkish speech‑to‑text model with 31.6 M parameters, built using a FastConformer encoder, a token‑and‑duration transducer (TDT), and a CTC head. It was trained from scratch on 4,977 hours of data—281 hours of human transcription and 4,696 hours of machine‑generated subtitles that were verified by a second reading—under licenses permitting commercial use. The model outputs cased, punctuated Turkish text with numbers written as words, achieving a normalised WER of 0.0921 on FLEURS Turkish and 0.0916 on Common Voice 27.0 Turkish, outperforming Whisper base and small models, and it is released under Apache‑2.0.
By Sait Furkan Teke (ufak AI)
O'ido is an inference engine that performs open‑vocabulary English speech recognition entirely on an ESP32‑S3 microcontroller, running a 13‑million‑parameter Conformer‑CTC model with int8 quantization and a 1.3‑million‑parameter language model. On LibriSpeech it achieves 3.70%/8.23% WER on test‑clean/test‑other, only 0.12 points below full precision, and outperforms the vendor’s recognizer and several laptop‑based tiny models. The firmware processes audio in about twice the duration of the input on a single core, with a streaming variant that delivers partial transcripts while speaking, though it is not yet real‑time.
whyItMatters":"This work demonstrates that sophisticated, open‑vocabulary speech recognition can run on inexpensive, low‑power microcontrollers without dedicated neural accelerators, expanding the reach of voice interfaces to devices that previously could only support keyword spotting or fixed command sets."
By Daniel Varela S\'anchez, Guillermo Mart\'in-Coello Ju\'arez