arXiv AI
Sep 2

VoiceLongMemEval: Do Assistants Remember How You Sounded?

VoiceLongMemEval (VLME) is a new benchmark that tests AI assistants on their ability to remember how users sounded by incorporating paralinguistic metadata—such as emotion labels, prosody descriptors, and voice events—into each conversational turn. The benchmark uses a three‑stage adversarial gate to ensure that models cannot succeed with transcript alone, revealing a significant affect gap: models gain 0.09 to 0.38 accuracy when provided with paralinguistic cues, and audio‑native models outperform standard ASR pipelines in extracting these signals. The dataset and code will be released upon acceptance.

By Ramit Pahwa, Parivesh Priye, Apoorva Beedu
arXiv Computation and Language
5d ago

PTC-Bias: Phoneme-Level Temporal Competition for Bias Retrieval and Post-Decoding Correction in Speech LLMs

PTC-Bias is a two-stage framework that improves contextual biasing in speech large language models by using phoneme-level temporal competition. In the first stage, PTC Retrieval performs frame-synchronous phoneme decoding to generate a compact shortlist of bias words and their speech intervals. The second stage, PTC Correction, applies a local competition between retrieved candidates and mismatched transcript spans within those intervals, reducing near-homophone and word-segmentation errors without extra SpeechLLM passes. Experiments on LibriSpeech demonstrate consistent gains across two SpeechLLMs, with PTC-Bias reducing B-WER by up to 23.9% relative to CTC-Filter while keeping U-WER nearly unchanged.

By Zhiqi Ai, Han Cheng, Shiyi Mu, Yongjin Zhou, Shugong Xu