Tag: #asr16 results
Hugging Face explores how effectively current voice agents handle bilingual speakers who switch languages mid-sentence. The research provides a benchmark for evaluating ASR models in real-world, multilingual scenarios.
This tutorial provides a comprehensive guide on fine-tuning the Nemotron 3.5 ASR model. It covers adapting the model to specific languages, specialized domains, or unique accents to improve transcription accuracy.
Hugging Face announces the addition of Benchmaxxer Repellant to the Open ASR Leaderboard. This update introduces a safeguard against benchmark maximizers in automatic speech recognition evaluations. It enhances the reliability of model comparisons on the platform.
Developer shares advanced prompt engineering techniques for ASR in full-duplex speech model MichiAI. Uses contextual prompts for categories like license plates or names, outperforming Deepgram's word boosting. Includes blog, Github, and examples for voice agents.
Grammarly is introducing a new speech-to-text feature designed to improve mobile writing by moving beyond raw transcription. The tool aims to help users compose polished messages on the go, addressing the friction of traditional mobile keyboards and standard dictation.
Startup seeks budget-friendly, self-hosted ASR like Whisper or Parakeet for chatbot MVP, avoiding external APIs due to security. Open to deployment challenges for compliance. Requests trade-offs on performance and ease.