---
title: "ASR"
description: "4 posts about ASR, summarised, each linking to the original."
---

# ASR
> 4 posts about ASR, summarised, each linking to the original.

## Articles

### [Granite Switch 4.1: Chat, Decisions, and Speech from One Endpoint](https://yomu.fyi/post/granite-switch-4-1-chat-decisions-and-speech-from-one-endpoint.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Kate Soule, Subhajit Chaudhury, Hendrik Strobelt, Luis A Lastras
- Published: Oct 15, 2026
- Type: Announcement

Granite Switch 4.1 models combine chat, general and specialized decisions, and speech-to-text from a single hosted endpoint. The 8B and 30B models include a new Decision aLoRA for general-purpose decision-making and a Guardian aLoRA that outperforms Jev 1.13 and other closed models on safety tasks. They also embed Granite-Speech-5.0-Turbo-CTC, the fastest ASR model on the OpenASR Leaderboard, enabling efficient transcription. All components are composed into one model using cache-efficient Activated LoRAs, taking up about the same GPU VRAM as the base model alone, and the KV cache from base generation can be reused for any subsequent adapter decisions. Granite Switch 4.1 can switch between chat, decision, and spoken requests quickly, and a 3B variant achieved 60-100 decisions per second playing Doom, compared to Jev’s 10, while also narrating and answering questions.


### [Introducing Falcon ASR](https://yomu.fyi/post/introducing-falcon-asr.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Abdul Muneer, Rishabh Saraf, Ramesh Gundluru, Shamsa Hamad, Lepauloux, Hakim Hacid
- Published: Oct 7, 2026
- Type: Announcement

The Technology Innovation Institute in Abu Dhabi released Falcon-ASR, a 1.6 billion parameter speech recognition model focused on Arabic, especially the Emirati dialect, while also supporting English, French, Spanish, and Portuguese. On the Open Universal Arabic ASR Leaderboard, Falcon-ASR achieved a 20.92% average word error rate across six test sets, outperforming the best published average of 23.17% in the reference snapshot. The model recorded a 22.73% word error rate and 10.19% character error rate on an internal Emirati evaluation, 4.07% better in word error rate than the next best system compared. Training data included background noise, overlapping speech, music, reverberation, telephony effects, speed, and pitch variations to handle diverse recording conditions. Falcon-ASR builds on the Falcon3-Audio architecture and provides word-level timestamps linking transcribed words to audio positions.


### [A Practical Recipe for Semi-Supervised Federated ASR: Online Pseudo-Labels with Server Update Stabilization](https://yomu.fyi/post/a-practical-recipe-for-semi-supervised-federated-asr-online-pseudo-lab.md)
- Company: [Apple](https://yomu.fyi/company/apple.md)
- Published: Sep 24, 2026
- Type: Problem & solution

Semi-supervised federated learning (SSFL) for ASR uses a teacher to generate pseudo-labels on unlabeled client data while the server trains on a small labeled seed. The paper finds that pseudo-label errors compound across the output sequence and training rounds, causing divergence and a large gap to fully-supervised FL. Two coupled design axes close this gap: the teacher type (per-client online, global broadcast, or transitioning) and the anchor (server-side labeled-data updates that stabilize training). A per-client online teacher diverges on its own but, once stabilized, matches or beats the global teacher in-domain and under domain shift. The server must keep training on labeled data between rounds; otherwise the online teacher drifts, and this interleaving governs convergence more than the seed model does. The proposed guidelines improve over the strongest prior method on 9 of 11 pairs, by 20.8% in-domain and 10.0% cross-domain on average.


### [\*\*Know Who Spoke When: Build Real-Time, Multi-Speaker AI with NVIDIA Nemotron 3 Diarization\*\*](https://yomu.fyi/post/know-who-spoke-when-build-real-time-multi-speaker-ai-with-nvidia-nemot.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Francesco, Ivan Medennikov, Taejin Park, Jagadeesh Balam, Adi- margolin, Maryam Motamedi
- Published: Sep 23, 2026
- Type: Announcement

NVIDIA Nemotron 3 Diarization is an open-weight, 100M-parameter model that ranks #1 on VoiceArena’s Diarization-Bench leaderboard with a 14.72% Diarization Error Rate (DER). It classifies who spoke when in conversations, handling overlapping speech and supporting up to eight speakers across live and recorded audio. The model uses a Sortformer approach with arrival-order speaker labeling, a 31-layer Transformer encoder, and two memory mechanisms for streaming inference. It accepts 16 kHz single-channel audio and outputs a floating-point tensor of speaker-activity probabilities at a configurable time step. Developers can combine its timestamps with automatic speech recognition to produce speaker-attributed transcripts, with recommended input-buffer latencies ranging from 0.32 to 30.4 seconds.