What are you looking for?

When Synthetic Speech Is All You Have: Better Call GRPO🔥 hot

AI·LLM-based ASR in regulated domains like banking is limited by privacy and real-speech collection costs; synthetic TTS data is a cost-effective substitute, but acoustic mismatch hinders supervised fine-tuning (SFT). Group Relative Policy Optimization (GRPO) applied solely to synthetic speech reduces WER by 40% relative to SFT (36.71% to 22.09%) and by 45% in the SFT-then-GRPO sequence, by improving behavioral calibration and audio attention rather than representations.

Diarization-Guided Qwen-ASR Adaptation for Multilingual Two-Speaker Conversational Speech

AI·Researchers developed SQZ-Qwen-ASR-1.7B for MLC-SLM 2026 Task 1, combining a modular speaker diarization front-end (VAD, CAMPPlus embeddings, spectral clustering, RTTM segmentation) with Qwen3-ASR-1.7B adapted via full supervised fine-tuning, LoRA on TTS-generated synthetic speech, and GRPO reinforcement learning for lower tcpMER (23.70 on dev set, 17.97 on eval set).

Best-of-$N$ TTS Evaluation is Confounded by ASR Family Alignment

AI·Best-of-N TTS evaluation is confounded by ASR family alignment, where verifier quality depends on the judging ASR model family.

Gemini 3.1 Flash TTS: the next generation of expressive AI speech

Our newest audio model introduces granular audio tags that give you precise control to direct AI speech for expressive audio generation.