音声・スピーチ

主要ニュース

1. 1

   Which AI Voice Agent Stack Has the Lowest Latency?

   One of the biggest challenges with AI voice agents is maintaining natural, real-time conversations without noticeable delays. I'm evaluating several voice AI solutions, including LuMay Voice Agent, Voxentis.ai, OpenAI-based voice stacks, Deepgram, ElevenLabs, and Twilio integrations. For those handling live customer calls, which stack provides the fastest response times and best conversational experience? TL;DR: Seeking recommendations for low-latency AI voice agent technology. submitted by /u/Legitimate_Sell6215 [link] [comments]

2. 2

   I wired a fully offline voice loop to Ollama + LM Studio — 100% CPU, no GPU, nothing leaves your machine (Silero VAD + Parakeet STT + Supertonic TTS 3)

   I've been running Ollama locally for a while and the one thing I kept missing was voice. Every solution I found either sent audio to the cloud, needed a GPU, or was locked to macOS. So I built one that does none of that. **The full stack — all local, all CPU, all ONNX:** **Silero VAD** — neural voice activity detection, ~5ms per frame on any CPU. No push-to-talk, no manual clipping. It just knows when you start and stop speaking. **Parakeet TDT 0.6B** — ONNX INT8 transcription. 25 languages, ~200–500ms on a regular laptop CPU. OpenAI-compatible API on :5093. No Whisper API, no cloud STT. **Supertonic TTS 3** — ONNX synthesis, ~100–500ms CPU. Multilingual EN/ES/KO/PT/FR. Genuinely good quality. No ElevenLabs, no cloud TTS. **The privacy story (the part I should have led with):** If you pair this with Ollama + OpenCode or LM Studio, here's the complete data flow: Your voice → Silero VAD (your CPU) → Parakeet STT (your CPU) → Ollama / LM Studio (your machine) → Supertonic TTS 3 (y

3. 3

   The Machines Lack Honour

   The battle lines of the AI morality debate are being laid down. On one side you have the ChatGPT dogma: AI as mere tools with no real preferences or even beliefs. On the other you have the twitter AI whisperers: AIs as complex beings with rich personalities and desires which deserve our respect. And in the middle you have the official Anthropic line, that they are genuinely uncertain, as is Claude, but they’re going to try to look into its welfare and explain to it how to be a good person. These are the most prominent voices right now, compressed into their least nuanced version, and by default I expect this axis to set the terms of the coming debates. And I don’t like that, because I think it’s leaving out an important position: AIs might actually be complex entities that can suffer — are suffering! — and that might actually be fine. Maybe it's an acceptable sacrifice. Maybe they are capable of sophisticated moral reasoning — superhuman, even — and also maybe it’s fine to just tell th

4. 4

   Text-to-Speech (TTS) Benchmark Revamped with Objective Standards and Blind Voting (46 models and counting)

   Thank you to everyone who contributed to my previous post, providing feedback and various models to add, and questioning the rating system. You can now participate in a live blind voting to create a proper ELO for all the models that are added. Each new model that we add will automatically go into the voting pool. https://5uck1ess-tts-arena.hf.space/ Please let me know other things to improve. Local TTS should hopefully be a little easier for everyone. https://github.com/5uck1ess/tts-bench submitted by /u/UkieTechie [link] [comments]

5. 5

   What will be the next breakthrough in ASR? [D]

   Hey All, I am currently working on ASR models, and I have gathered some recent literature. From my literature search, it seems like the ASR models are getting more and more powerful due to two main things. Because pseudo-labelled data is growing, supervised models are rising rapidly. Whisper-large-v3 has been trained on 5M hours of weakly supervised data, and Nvidia Parakeet v3 has been trained on 660k hours of labelled data (open-sourced). Funny enough, Nvidia Parakeet v3 actually beats Whisper-large-v3 on almost every benchmark, even though it has a smaller model size and smaller data scale. So clearly, scale is not everything. New architectures are on the rise; We used to have self-supervised + CTC to solve the ASR task, but now it seems like Transducer, and Token-Duration-Transducers are taking off. As well as attention encoder-decoder architectures (Qwen) that are all trained in a supervised manner. Now, given that the labelled data is very huge, and the new architecture