Recently, large language models (LLMs) have achieved promising progress in the fields of classical Chinese translation and the generation of classical poetry. However, domain-specific research on precise translation and affective-semantic understanding of classical poetry remains limited.
arXiv:2606. 12392v1 Announce Type: cross Abstract: Recently, large language models (LLMs) have achieved promising progress in the fields of classical Chinese translation and the generation of classical poetry.
PARSA‑Bench is the first dedicated benchmark for evaluating large audio‑language models on Persian, addressing unique challenges such as classical poetry, traditional music, and code‑switching. It comprises 16 tasks—10 of which are new—covering speech understanding, paralinguistic analysis, and culturally grounded audio reasoning. Across most tasks, text‑only baselines outperform audio‑based models, indicating that audio understanding remains the main limitation, except for Persian poetry where prosody provides additional information that audio beats text.
arXiv:2606. 29273v1 Announce Type: cross Abstract: Emotion recognition of song lyrics is a challenging task since lyrics may not necessarily align with the overall emotion of a song.
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.
arXiv:2608.23098v1 Announce Type: new Abstract: Classical Chinese poetry composition has long valued Tuiqiao, the iterative refinement of words, imagery, and prosody. However, many current AI poetry...
arXiv:2608. 11452v1 Announce Type: cross Abstract: Text-to-image (T2I) models are increasingly asked to illustrate literary and cultural content, yet we cannot measure how well an image renders the meaning of a poem.
arXiv:2608.28986v1 Announce Type: new Abstract: LLMs often struggle with modern Korean poetry, producing outputs that resemble "line-broken prose." We address two coupled tasks: detecting whether a K...
arXiv:2505. 18614v5 Announce Type: replace-cross Abstract: Lyrics translation requires both accurate semantic transfer and preservation of musical rhythm, syllabic structure, and poetic style.
arXiv:2510.10774v4 Announce Type: replace-cross Abstract: Persian remains substantially underrepresented in open speech-text resources, limiting progress in multi-speaker text-to-speech (TTS), speech...
While large language model (LLM)-based text-to-speech (TTS) systems have achieved high-quality speech synthesis, most existing systems focus on English and Chinese. Japanese, however, remains under-explored, and its unique linguistic challenges, such as widespread context-dependent kanji polyphony, have yet to be adequately tackled.
The paper introduces Padyam2Gadyam, a dataset of 600 13th‑17th Century Telugu poems paired with human‑verified Telugu and English prose translations. It evaluates two traditional machine translation systems and five large language models on zero‑shot poem‑to‑prose translation, finding that general‑purpose LLMs outperform the MT systems but still exhibit systematic issues in generating and evaluating prose translations.