VQV Signal
According to a post on X by @TONGYI_SpeechAI, Alibaba's Tongyi speech team trained Qwen-Audio-3.1-Realtime to read emotion and acoustic cues, infer intent and write a short response plan before replying. The training used supervised fine-tuning followed by GRPO. The post says the Qwen-Audio-3.1-Realtime-Plus version tops the EchoMind, Stage3 and Test-Human empathy benchmarks, and that a technical report is now out.
According to a post on X by @TONGYI_SpeechAI, Alibaba's Tongyi speech team trained Qwen-Audio-3.1-Realtime to read emotion and acoustic cues, infer intent and write a short response plan before replying. The training used supervised fine-tuning followed by GRPO. The post says the Qwen-Audio-3.1-Realtime-Plus version tops the EchoMind, Stage3 and Test-Human empathy benchmarks, and that a technical report is now out.
AI-assisted summary based on the listed source.
Alibaba's Tongyi speech team trained the model to read emotion and acoustic cues, infer intent and write a short response plan before replying, using supervised fine-tuning then GRPO. It says Qwen-Audio-3.1-Realtime-Plus tops EchoMind, Stage3 and Test-Human empathy benchmarks; a technical report is out. Why it...
The approach targets voice assistants that respond to how something is said, not only to the words. Developers and businesses building speech products may want to read the technical report to see how the benchmark results were measured. The benchmark claims come from the team itself, so independent testing will be the next thing to watch.
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VQV surfaced this signal because it is recent, relevant to Open Source LLMs, connected to Scobleizer News Pulse (X).
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