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Preprint Aug 2026

DiaScriber: A Speech LLM for Joint Diarization and Transcription in Multi-Speaker Scenarios

DiaScriber is proposed, an end-to-end multi-speaker diarization and transcription model built on a speech large language model that achieves superior performance over comparison methods across extensive multi-speaker scenario test sets and demonstrates outstanding generalization ability in unseen multi-speaker scenarios.

Bing-Shen Mu, Xian Shi, Xiong Wang et al. · 0 citations
Preprint Jul 2026

Diarization-Guided Qwen-ASR Adaptation for Multilingual Two-Speaker Conversational Speech

Results show that supervised fine-tuning provides the largest gain, while synthetic-speech LoRA adaptation and reinforcement learning further improve robustness, while synthetic-speech LoRA adaptation and reinforcement learning further improve robustness.

Hao Wu, Rong-Qi Han, Zhen Wang et al. · 0 citations
Open access Jul 2026

A Nepali-Accented English Evaluation Dataset for Automatic Speech Recognition

Automatic speech recognition (ASR) systems perform strongly on native-English benchmarks, yet their accuracy degrades sharply when the input speech comes from under represented non-native accents. Nepali-accented English is particularly under-served: existing resources either focus on native Nepali speech, cover broader multi-accent settings without dedicated Nepali evaluation, or provide only limited Nepali-accent coverage. This paper presents a Nepali-accented English evaluation dataset designed to support robust ASR benchmarking under accent mismatch. The corpus was collected through a web-based platform that did not collect directly identifying metadata and contains recordings from 57 speakers. Each session follows a fixed 22-prompt protocol consisting of 11 phonetic prompts, 10 domain prompts, and 1 spontaneous prompt, providing complementary coverage of pronunciation, topical vocabulary, and natural speaking style. In addition to transcribed speech, the dataset includes participant metadata for coarse exploratory subgroup analysis and speaker-level manual recording-quality labels. Manual quality assessment shows that 50.9% of sessions are clean and 42.1% contain only mild noise. As a descriptive reference, open-source ASR baselines are substantially worse on this corpus than the corresponding LibriSpeech test-clean values reported in official model cards, reaching 38.15–55.00% WER on the collected set versus reported 2–4% WER on LibriSpeech test-clean. These baseline results position the corpus as a practical held-out resource for evaluating accent robustness and out-of-distribution generalization on Nepali-accented English.

Santosh Dahal, K. Dahal · 0 citations
Preprint Aug 2026

SraVaani 1.0: Scaling Inclusive Speech Recognition for Indic Languages

SraVaani-1.0 achieves the lowest word error rate (WER) on a large number of language-dataset pairs while remaining competitive with the best-performing systems on high resource while being assessed exclusively on the VAANI benchmark.

Sujith Pulikodan, A. Basu, J. Pavankumar et al. · 1 citation · ⚡1
Preprint Jul 2026

DONDO: Open w2v-BERT Speech-Recognition Base Models for African Languages

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. DONDO comprises twenty-one monolingual models and five multilingual models spanning twenty-seven language varieties across Ghana, Sierra Leone, Nigeria, Senegal, Kenya and Zimbabwe. Models are fine-tuned primarily on read speech drawn from religious texts, which offer broad, license-clear and orthographically consistent coverage for languages that otherwise lack transcribed audio. We describe a two-step (and, for one family, three-step) learning-rate-annealed fine-tuning procedure that first adapts a shared multilingual model at a high learning rate and then anneals it to recover, and in several cases surpass, strong monolingual baselines. We further describe a lightweight language-conditioning mechanism that injects a one-hot language identity as a sequence of prefix frames prepended to the acoustic features, allowing a single multilingual checkpoint to be steered to a target language at inference. Across the five multilingual families the annealed models reach average word error rates (WER) of 10-13%, closing most of the gap to monolingual models while covering many languages in a single checkpoint. All models are released on the Hugging Face KhayaAI organisation under the Apache-2.0 license (attribution only) so that others may fine-tune them freely, including for commercial use. We provide a conservative estimate that the languages covered are spoken by on the order of one hundred million first-language speakers, and by substantially more when second-language use is included.

P. Azunre, N. Ibrahim, Joel Budu et al. · 0 citations

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