The paper introduces a trilingual spoken hallucination detection benchmark covering English, Russian, and Kazakh news, with 12,013 samples that include synthetic alterations and severity levels, as well as 290 fact‑checked misinformation items. Detectors are evaluated in a reference‑free setting on text, ASR transcripts, and audio, revealing that most models underperform baseline classifiers, except Gemma‑3n on transcripts. Synthetic‑trained detectors achieve high macro‑F1 scores on real‑world misinformation, but Russian provenance analysis highlights model‑dependent signals that confound synthetic benchmarks.
By Meruyert Aristombayeva, Jason S. Lucas, Chaewan Chun, Dongwon Lee
The paper introduces VeriSpeak, a benchmark of 3,879 spoken claims for evaluating fact verification in Large Audio Language Models (LALMs). It shows a clear modality gap: models that verify written claims well often fail on spoken versions, and retrieval alone offers limited improvement. Combining retrieval with explicit reasoning yields the best performance, reaching 86.1% accuracy and demonstrating the need for grounded reasoning over retrieved evidence in speech misinformation detection.
By Debajyoti Mazumder, Mamta, Abhirama Subramanyam Penamakuri
While text-based hallucination detection has been extensively studied, spoken hallucination detection remains largely unexplored, particularly for low-resource languages. We present the first multilin...
The paper introduces MAD2, a synthetic benchmark of 1,000 two‑speaker dialogues with about 10 hours of audio and 1,230 check‑worthy sentence annotations for spoken claim verification. It proposes a calibrated multimodal fusion approach that combines a context‑aware audio encoder with a dialogue‑aware text model. Experiments show that adding dialogue context improves verification performance, though the gains differ across scenarios, and that fusion offers the largest advantage when full‑dialogue context is available, though it does not consistently outperform text alone.
By Chaewan Chun, Delvin Ce Zhang, Dongwon Lee
M3-DuplexBench is a new benchmark designed to evaluate full‑duplex spoken dialogue systems (FDSDSs) that can listen while speaking. It covers multi‑turn interactions in both English and Japanese across casual conversation and question‑answering domains, jointly assessing interaction timing and response content. The benchmark also offers a reference‑conditioned setting to test sensitivity to dialogue history and trajectory mismatch.
By Ryo Fukuda, Atsushi Ando, Hiroki Kanagawa, Takatomo Kano, Marc Delcroix, Naohiro Tawara, Yuya Chiba
arXiv:2609.14542v1 Announce Type: new
Abstract: Neyshekar is presented as an open Persian read-speech corpus designed for coverage of both formal and informal language, named entities, and longer utt...
By Ahmad Amirivojdan, Farzad Nadiri, Abolfazl Alizadeh, Shaghayegh Yaraghi
The paper introduces a new benchmark of 13,044 spontaneous Nepali‑English code‑switched podcast utterances, totaling 15.45 h, and evaluates a two‑stage synthetic‑to‑real adaptation framework for ASR. Stage 1 uses supervised fine‑tuning on 0.95 h of synthetic speech, reducing Mixed Error Rate from 23.40 % to 21.52 % and switch‑point error rate from 35.89 % to 28.77 %. Stage 2 applies Group Relative Policy Optimization with script and transcription rewards, yielding modest but statistically significant further improvements, particularly in English script confusion and switch‑point accuracy. The study also shows that an auxiliary script reward term offers no benefit beyond MER‑only optimization in this setting, and that the synthetic SFT provides the main cross‑domain transfer to spontaneous human speech.
By Firoj Paudel
arXiv:2609.09889v1 Announce Type: new
Abstract: Automatic Speech Recognition (ASR) technology is fundamental to customer service automation and large-scale transcription. However, even advanced ASR m...
By Yonghyun Jun, Jimin Lee, Hwan Chang, Dongho Shin, Seolah Kim, Hwanhee Lee
arXiv:2611.06812v1 Announce Type: new
Abstract: With the growing need for evaluating Large Language Models (LLMs) and their applications to speech, challenges persist in summarizing and evaluating co...
By Man-Ling Sung, Hemanth Kandula, Jeff Ma, William Hartmann, Matthew Snover
arXiv:2609.23416v1 Announce Type: cross
Abstract: Long-form audio performance is often summarized by context length and aggregate accuracy, obscuring how language, evidence, and task jointly shape di...
By Zeyu Yang, Xinyu Zhang, Zibo Bi, Pei Zhang, Xize Cheng, Jin Xu, Baosong Yang, Satoshi Nakamura
Recent advances in zero-shot text-to-speech (TTS) have substantially improved speech quality and voice cloning fidelity. However, many zero-shot TTS systems still depend on audio prompt transcripts at inference time.
The paper introduces ECHO, a paired diagnostic benchmark for evaluating Chinese real‑time spoken dialogue systems on turn‑taking. ECHO pairs examples that share the same overlap transcript but differ in preceding multi‑turn context, requiring either Yield or Keep actions, and also includes off‑talk cases to test unnecessary yielding. Experiments on four speech systems reveal that three systems over‑yield, correctly keeping the floor on fewer than 13% of backchannels, while the fourth system shows a more balanced performance, illustrating that interruption‑only evaluation can overestimate turn‑taking reliability.
By Shuofeng Zhao, Hongwei Cai, Wenke Fan, Qingxiang Guo, Zhou Wang, Dawei Yang, Zhiyang Zhou, Yingxin Shang, Weixu Wang, Lin Yang, Shuran Zhou, Yang Song