@seclink: Clean English speech is already commoditized; the challenges for ASR in 2026 include recognizing speakers in overlapping audio, committing to real-time transcription, handling dialects and terminology, and trimming the speech tower without dragging down Speech LLM.
Summary
Clean English speech is commoditized, but in 2026, ASR faces challenges such as overlapping speech recognition, real-time transcription, dialect and terminology handling, and optimizing the speech tower to avoid burdening Speech LLM.
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@aigclink: Alibaba Tongyi Lab just dropped Fun-ASR 1.5—one industrial-grade model handles 30 languages, all 7 major Chinese dialect families + 20+ regional accents, even classical-poetry recitation. Dialect CER down 56.2 % vs last gen; 5 dialects top 90 % accuracy…
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.
@FeitengLi: Actually, these problems can be well solved: 1. Ditch whisper, switch to an ASR model. Qwen3-ASR is great with few hallucinations, and there are other ASR options. Whisper has many hallucinations and requires 30s segments. Qwen3-ASR gets more accurate with longer audio, supporting up to 20…
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.
@MaxForAI: If you are working on voice agents, you should try this project. A team from NTU, NUS, and Shanghai AI Lab released: Mega-ASR. This fully open-source ASR is built on Qwen3-ASR, aiming to break the long-standing bottleneck of ASR performance in noisy, reverberant, or other impaired real-world environments...
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.
@FeitengLi: Next week, after adding speaker labeling and speech generation, it won't be this cheap early bird price anymore.
EdgeSpeak officially launched, a local-first, privacy-preserving accurate transcription tool, supporting semantic segmentation and timestamps, compatible with OpenAI Audio API, etc. It will later add speaker labeling and speech generation features.
ASR-Roundtrip Evaluation Can Mask Context- and Convention-Dependent Reading Errors in Chinese News TTS
This paper studies a failure mode in ASR-roundtrip evaluation for Chinese news TTS, showing that fluency errors in context-dependent reading decisions can be masked by ASR surface recovery. A targeted audit across TTS and ASR systems quantifies these false negatives and proposes a human-audited protocol.