The study fine‑tunes the Whisper Small model for Automatic Speech Recognition (ASR) in Baniwa, an indigenous Arawakan language. Using a 0.54‑hour corpus of 1,373 manually transcribed recordings, the fine‑tuned model achieved a Word Error Rate of 37.5% and a Character Error Rate of 7.45%. These results provide an initial baseline for Baniwa ASR and suggest that multilingual foundation models can be adapted to extremely low‑resource languages.
By Leonardo Duart, Tiago Fonseca, Thiago Chac\'on
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.
By Gabrial Zencha Ashungafac, Busayo Awobade, Tobi Olatunji
The paper introduces methods to improve speech recognition for multilingual video transcription using Whisper-based tools, targeting cross‑cultural understanding. It reports an average transcription error rate of 30% across seven languages, which can be lowered to 20% with modest fine‑tuning. The authors also release associated speech and metadata to aid community refinement of these techniques.
By Michael Picheny
The paper introduces a synthetic Bengali speech dataset tailored for telecom customer‑care applications, comprising 10,000 audio‑text pairs (≈26.82 hours) with predefined train, validation, and test splits. The data were generated using OmniVoice voice‑cloning, and include both original and normalized transcripts for ASR/STT use. Automatic intelligibility evaluation with a fine‑tuned Whisper model shows an average WER of 2.54% and CER of 0.59%, indicating strong text‑audio consistency, while the authors note limitations of synthetic speech and STT‑based evaluation.
By Kawshik Kumar Paul, Md. Nafiul Alam Fuji
arXiv:2609. 11786v1 Announce Type: new Abstract: Automatic speech recognition (ASR) systems and audio language models (audio LMs) now report low error rates on monolingual benchmarks, but their behavior on code switched speech in low resource, diacritic rich languages remains poorly characterized.
By Chibuzor Okocha, Christan Earl Grant
arXiv:2306. 11252v2 Announce Type: replace-cross Abstract: We introduce HK-LegiCoST, a new three-way parallel corpus of Cantonese-English translations, containing 600+ hours of Cantonese audio, its standard traditional Chinese transcript, and English translation, segmented and aligned at the sentence level.
By Cihan Xiao, Henry Li Xinyuan, Jinyi Yang, Dongji Gao, Matthew Wiesner, Kevin Duh, Sanjeev Khudanpur
The paper describes Transsion Speech Team’s submission to Task 1 of the MLC‑SLM 2026 Challenge, aiming at speaker‑attributed transcription for multilingual conversational speech. Their cascaded framework includes a DiariZen‑based speaker diarization module, a Qwen3‑Omni‑based long‑form multilingual ASR module with CTC alignment for precise timestamps, and a fusion module that merges diarization and transcription outputs into speaker‑attributed STM results. On the official evaluation set, the system achieved a tcpMER of 15.41% and secured second place among all participants.
By Zhecheng Ren, Xuanji He, Xiaoxiao Li, Zhichen Han, Gaoyang Dong, Gaosheng Zhang, Minchuan Chen, Fengjie Zhu
arXiv:2610.08063v1 Announce Type: cross
Abstract: This paper presents the HINTT system submitted to the 2nd Challenge and Workshop on Multilingual Conversational Speech Language Model (MLC-SLM). We a...
By Takanori Ashihara, Kohei Matsuura, Masato Mimura
The paper evaluates bias in phoneme-based automatic speech recognition (ASR) systems, focusing on WhisperIPA and ZIPA, which produce International Phonetic Alphabet (IPA) transcriptions. Using multilingual speech corpora and demographically annotated English datasets, the authors compare model-generated IPA against grapheme-to-phoneme (G2P) outputs with both standard phoneme error rate (PER) and a new Soft PER metric that allows linguistically similar substitutions. The study finds persistent disparities across language, gender, accent, ethnicity, and age, even when accounting for acceptable phonemic variation.
By Maneesha Rani Saha, Catherine Bao, Neal Patwari
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.
By Deovrat Mehendale, Aditya Mehndiratta, Dhruv Rathi, Kaushal Bhogale, Mitesh M. Khapra
The paper introduces a new benchmark of 13,044 spontaneous Nepali‑English code‑switched podcast utterances, totaling 15.45 h, and evaluates a two‑stage synthetic‑to‑real adaptation framework for ASR. Stage 1 uses supervised fine‑tuning on 0.95 h of synthetic speech, reducing Mixed Error Rate from 23.40 % to 21.52 % and switch‑point error rate from 35.89 % to 28.77 %. Stage 2 applies Group Relative Policy Optimization with script and transcription rewards, yielding modest but statistically significant further improvements, particularly in English script confusion and switch‑point accuracy. The study also shows that an auxiliary script reward term offers no benefit beyond MER‑only optimization in this setting, and that the synthetic SFT provides the main cross‑domain transfer to spontaneous human speech.
By Firoj Paudel
arXiv:2605. 19266v2 Announce Type: replace-cross Abstract: Automatic speech recognition (ASR) systems are typically optimized for verbatim transcription, which preserves disfluencies, filler words, and informal spoken structures that are often unsuitable for downstream writing-oriented applications.
By Wanyi Ning, Yinshang Guo, Haitao Qian, Jiyuan Cheng, Weiyuan Feng, Yufei Zhang