asr

This paper proposes a dialect-aware phonetic framework for modeling phonetic variation in Vietnamese ASR, decomposing syllables into structured components and mapping them to dialect-specific IPA representations. The approach matches pretrained baselines with fewer parameters and no external pretraining on the UIT-ViMD multi-dialect dataset.

NTU, NUS, and Shanghai AI Lab jointly released Mega-ASR, a fully open-source ASR model built on Qwen3-ASR. Using the Voices-in-the-Wild-2M dataset and progressive acoustic-to-semantic optimization, it achieves up to 30% relative Word Error Rate (WER) reduction in real-world noisy environments. With only 1.7B parameters, it enables efficient inference on consumer-grade hardware.

This paper introduces CLD, a lightweight convex optimization-based language detection head for ASR that achieves 97-98% accuracy with under 100 training samples while reducing compute costs by 13x, addressing accent and dialect robustness across 5 languages and 24 sub-dialects.

StepAudio 2.5 is a unified audio-language model that achieves state-of-the-art results across ASR, TTS, and real-time spoken interaction by leveraging task-tailored reinforcement learning from human feedback to optimize shared representations.

Mega-ASR is a 1.7B parameter robust ASR model under Apache 2.0, designed for noisy, reverberant, and overlapping speech, with an audio quality router to handle clean vs degraded audio.

SCRIBE is a diagnostic evaluation framework for automatic speech recognition that provides categorical error decomposition for Indic languages, releasing benchmarks and open-weight rich transcription models for Hindi, Malayalam, and Kannada.

Open sourcing Mega-ASR, a full-scenario SOTA industrial-grade ASR model designed for challenging audio conditions like far-field and noise, outperforming existing open and closed models by 10-30% on real-world benchmarks.

StepFun launches Step Plan subscription at $6.99/month, integrating LLM, TTS, ASR, image generation, and other AI models. Supports direct OpenAI SDK connection, applicable for voice cloning, meeting transcription, AI podcast generation, etc.

This paper presents a benchmark evaluating five commercial ASR systems on code-switching speech across Arabic-English, Persian-English, and German-English pairs, using a two-stage pipeline to select 300 samples per pair and assessing performance with WER and BERTScore. ElevenLabs Scribe v2 achieves the lowest overall WER (13.2%) and highest BERTScore (0.936), with public dataset available.

Mega-ASR proposes scaling up real-world acoustic simulation to improve automatic speech recognition in challenging, wild conditions, aiming to narrow the performance gap between lab and real-world settings.

NVIDIA releases Nemotron 3.5 ASR, a 600M parameter multilingual streaming speech recognition model supporting 40 language-locales with a Cache-Aware FastConformer-RNNT architecture for low-latency transcription. The model supports configurable chunk sizes and is ready for commercial use under the OpenMDW-1.1 license.

Recommends using Qwen3-ASR instead of Whisper to reduce hallucinations, using LattifAI tools for precise audio-text alignment and subtitle generation, and introducing their own OmniVAD-Kit project for voice activity detection.

Violin is an open-source end-to-end video translation and video Q&A tool, integrating ASR, LLM translation, and TTS. It supports style adjustment and content re-creation, and can answer questions about video content.

This paper presents a calculus-based framework that uses first and second derivative tests to estimate the optimal vocabulary size hyper-parameter for end-to-end ASR systems, improving performance on the Librispeech corpus.

Violin is an open-source video translation tool that integrates speech recognition, large language model translation, and text-to-speech. It supports over 30 languages and offers three usage modes: CLI, web app, and Claude Code.

Introduces Vividh-ASR, a complexity-tiered benchmark for Hindi and Malayalam ASR, identifies studio-bias in fine-tuning, and proposes R-MFT to improve spontaneous speech performance efficiently.

Dolphin-CN-Dialect is a streaming-capable ASR model that improves dialect recognition through temperature-based sampling and redesigned tokenization, achieving competitive performance with a smaller model size.

Hugging Face announces the addition of private, high-quality datasets from Appen and DataoceanAI to the Open ASR Leaderboard to prevent benchmaxxing and test-set contamination, while maintaining public data for the default average WER calculation.

Researchers introduce Voice of India, a 536-hour closed benchmark of unscripted telephonic conversations across 15 Indian languages and 139 regional clusters, exposing geographic and demographic ASR performance disparities.

Alibaba Tongyi Lab releases Fun-ASR 1.5: a single model covering 30 languages, seven Chinese dialect groups and 20+ local accents; character-error rate in key dialect scenarios falls 56.2 %, with five dialects exceeding 90 % accuracy.