Related reading
Building and Evaluating Fixed-Voice Thai TTS from Synthetic Speech
Researchers train a compact 82 M‑parameter Thai fixed‑voice TTS model using synthetic speech generated by a large voice‑cloning teacher, requiring only a 15‑second real reference. The student achieves 68.2% keyword accuracy and 91.4% pause precision, outperforming its teacher on pause placement and enabling on‑device inference.
Hugging Face Daily Papersarxiv.org1 minpaperStepAudio 3 Realtime Technical Report
StepAudio 3 Realtime is an audio‑language foundation model that runs a continuous listen‑converse‑think‑act loop. It introduces Deep Perception for rich acoustic cue extraction, Seamless Duplex for handling pauses/back‑channels, and a Think‑While‑Speaking mechanism that lets the model reason in parallel with speech output. On benchmarks it scores 73.0 macro avg on StepAudioChat, 90.6 on MMSU, 98.…
Hugging Face Daily Papersarxiv.org2 minpaperOmniVChat: Synthesizing, Benchmarking, and Training for Native Audio-Visual Dialogue
OmniVChat defines native audio‑visual dialogue where a model consumes raw audio and video streams and replies in text. The authors build OmniVChat‑Studio, a multi‑agent simulator that generates single‑ and multi‑turn audio‑visual conversations, and use it to create OmniVChat‑Bench, a benchmark covering five dialogue abilities. They also propose OmniVChat‑RL, a reinforcement‑learning reward that b…
Hugging Face Daily Papersarxiv.org2 minpaperVākQA: A Benchmark and Evaluation Study for Telugu Spoken Factoid Question Answering
The authors present VākQA, a 2,001‑question spoken factoid QA benchmark for Telugu with audio, transcriptions, and human‑verified answers, and they validate automatic evaluation methods against human ratings. Using this setup they show that translation loses cultural nuance, ASR errors alter meaning, and cascaded ASR‑MT errors degrade model performance.
Hugging Face Daily Papersarxiv.org1 minpaperHow to Orchestrate Multi-Call Conversations with an LLM and Twilio Conversation Memory
A step‑by‑step tutorial for building a C# ASP.NET Core service that uses Twilio Conversation Memory and OpenAI’s gpt‑4o‑mini to retain caller context across multiple phone calls, with full code snippets, environment setup, and prompts for both real‑time response streaming and call summarization.
Twiliotwilio.com16 minVerifiable Social Reasoning for LLM Assistants
The paper introduces Fuse, a multi‑agent simulation that gives LLM assistants a verifiable ground‑truth task for social reasoning by hiding a target agent’s motive and letting a user‑mediated conversation infer it. Experiments on 12 LLMs show user mediation makes reasoning harder, models are biased by user framing, need more detail than humans, and longer chats don’t always help.
Hugging Face Daily Papersarxiv.org1 minpaper
