asr

parakeet.wgsl enables fast, accurate NVIDIA Parakeet TDT 0.6B V2 speech transcription entirely in the browser using raw WebGPU compute shaders and SIMD WASM, with a live demo and open-source library.

This paper compares context biasing methods and speech LLMs for recognizing rare and new words in automatic speech recognition, reporting trade-offs in word error rate across read and non-read speech.

A developer asks what STT APIs people use in production voice agents, comparing Deepgram, AssemblyAI, and Smallest AI Pulse, and highlighting common failure points like endpointing, latency, and barge-in.

OpenAI and Microsoft released GPT-transcribe and GPT-live-transcribe in Microsoft Foundry, offering high-accuracy asynchronous transcription and low-latency streaming transcription for recorded and live audio, with features like background noise handling, accent robustness, and alphanumeric perception.

Audio8's open-source ARK-ASR-3B claims the #1 spot on the Hugging Face Open ASR Leaderboard with a 4.76 Mean WER, while their 0.6B model also ranks in the Top 5, showcasing a competitive open speech stack.

Voice Memory introduces an inference-only scheme for agentic speech recognition where a frozen corrector uses a per-domain memory file to decide per utterance whether to act or abstain, reducing word error rate across multiple domains without weight updates.

This paper presents the first systematic study of federated training for SpeechLLM-based end-to-end ASR systems, evaluating on English and Italian tasks and achieving competitive word error rates with reduced communication costs.

Microsoft releases VibeVoice-ASR-BitNet, a compressed multilingual ASR model for real-time CPU inference. It achieves 1.6-2.3x faster inference than Whisper.cpp with real-time capability on as few as 3 CPU threads.

MoLGE assigns dedicated expert modules to clusters of similar languages in a mixture-of-experts framework for large-scale multilingual ASR, achieving improvements across 495 languages with minimal parameter increase.

Indic DiarBench is a multilingual joint diarization and ASR benchmark covering all 22 scheduled languages of India with 108 hours of human-corrected multi-speaker audio, capturing conversational nuances like code-mixing and speaker overlap.

MEUSLI is an open-source multilingual projector family that connects Whisper encoder with multilingual LLMs, enabling end-to-end ASR in 28 European languages and extending to speech translation and topic identification.

WeNet is an open-source end-to-end speech recognition toolkit that unifies training code, pretrained models, and deployment runtimes in one project, supporting multiple model architectures and runtime backends.

This paper compares state-of-the-art ASR systems to human listeners on recognizing diverse Dutch speech, finding that ASR systems match or exceed human performance in some cases, with Google Telephony leading. It highlights the impact of speaker age, regional accents, and test set selection on benchmarking conclusions.

This paper addresses the uncontrolled latent variable of transcription style in ASR models by introducing a method using coverage-aware decoder task tokens to enable controllable verbatim transcription with accurate word-level timing, achieving high disfluency detection F1 via zero-shot cross-lingual transfer.

Microsoft Research announced expanded coverage for their ASR model, adding 22 languages across 38 African countries, with new datasets and test samples.

Microsoft Research announces the second release of PazaBench, a benchmark for evaluating automatic speech recognition models across African languages.

Details a method to run a 13 million parameter ASR Conformer model directly on a microcontroller, highlighting advances in edge AI deployment.

MOSS-TD, a speaker-aware ASR system, is optimized within the SGLang-Omni serving stack, enabling 38-minute multi-speaker audio to be transcribed in about 49 seconds on a single H100, with concurrent processing of 16 meetings.

MOSS-Transcribe-Diarize, an open-source ASR model with multi-speaker diarization and hotword biasing, trends on Hugging Face after release.

NVIDIA open-sourced a 600M parameter model that transcribes 40 languages in real-time with 80ms latency, supporting multiple languages from a single checkpoint with built-in punctuation and capitalization.