arXiv:2606. 07533v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) effectively integrate text and audio to interpret context in complex interactive dialogues.
This paper investigates whether prosodic features—pitch, energy, and timing—are preserved when speech is translated between languages. Using multilingual dubbing data for English‑German, English‑Spanish, and English‑French pairs, the authors conduct a fine‑grained cross‑lingual analysis to quantify similarities and differences in prosody. The study identifies inherent cross‑lingual correlations in prosodic structure and explores how linguistic and alignment factors influence these patterns.
The paper investigates whether audio‑language models capture paralinguistic cues beyond spoken content. Using the Expresso dataset and four open‑source models, the authors trace how speaking style information is encoded in the late layers of the audio encoder but is degraded before reaching the final output. They find that some models are content‑driven while others are acoustic‑driven, revealing a gap between what is encoded and what is utilized in current audio‑language models.
arXiv:2609.16396v1 Announce Type: new Abstract: Negation is typically modeled through its linguistic realization, although spoken interaction is accompanied by tightly coordinated nonverbal behavior....
The paper explores the reverse of the common prosody-from-text task by predicting text from low‑frequency speech features. Using a Whisper model fine‑tuned on only the 12 lowest Mel bins (≈450 Hz cutoff), the authors achieve a word error rate of 36 %, with 10 % of utterances perfectly recovered and 40 % below 25 % WER. They also report that, given the correct prefix, the next token is predicted correctly 79 % of the time, indicating a stronger link between low‑frequency prosody and lexical content than previously assumed.
The study investigates how multimodal large language models (MLLMs) use prosodic cues in sarcasm detection. By testing Qwen2.5‑Omni and Qwen3‑Omni on Mandarin Chinese and English across five modality conditions, the authors find that adding audio increases false positives without improving true positives. Acoustic error analysis shows that models rely on a stereotypical prosodic pattern—elevated pitch and irregular pausing—that does not align with genuine sarcasm cues, and manipulating these dimensions alone can raise false positive rates up to 60%. The same effect appears in Gemini 3 Flash Preview, indicating the heuristic is not limited to a single architecture.
DEAF (Diagnostic Evaluation of Acoustic Faithfulness) is a benchmark comprising over 2,700 conflict stimuli that test Audio Multimodal Large Language Models (Audio MLLMs) across three acoustic dimensions: emotional prosody, background sounds, and speaker identity. The benchmark uses a controlled multi-level evaluation framework that gradually increases textual influence, from semantic conflicts to misleading prompts, to separate content-driven bias from prompt-induced sycophancy. Diagnostic metrics are introduced to measure how much models rely on textual cues versus acoustic signals, and an evaluation of seven Audio MLLMs shows that while models respond to acoustic variations, their predictions are largely driven by textual inputs, highlighting a gap between benchmark performance and true acoustic understanding.
Multimodal Large Language Models (MLLMs) process speech and text jointly, yet whether they exploit prosodic cues for pragmatic inference or rely on surface acoustic patterns has received little system...
arXiv:2609.22452v1 Announce Type: new Abstract: Long-context understanding remains a fundamental challenge for large language models, as excessively long inputs often lead models to forget salient in...
arXiv:2603.23938v2 Announce Type: replace Abstract: Most testbeds for omni-modal models assess multimodal understanding via textual outputs, leaving it unclear whether these models can properly speak...
The paper investigates why AI-generated text has become harder to understand, focusing on psycholinguistic features linked to human processing difficulty such as object relative clauses, long syntactic dependencies, and low-frequency n-grams. Using a new corpus of AI-generated text from diverse domains, the study finds that frontier and near-frontier language models employ these difficult features more frequently than paraphrases from round-trip translation and human writing, including more inanimate subjects with animate verbs, longer dependencies, stacked nouns, and reduced object relatives. The authors hypothesize that AI models prioritize information density for tasks like math, code, and reasoning, diverging from human communication goals, and demonstrate this by fine‑tuning a model with a length penalty, which reduces human‑like linguistic behavior.
arXiv:2609.14743v1 Announce Type: new Abstract: Pure speech language models often lag behind text and speech-text language models in generating coherent content, but this gap is difficult to quantify...