Paper

Afrispeech Semantics: Evaluating Audio Semantic Reasoning in Spoken Language Models Across Domains and Accents

Unreadunread

AI summary

arXiv:2606. 11219v1 Announce Type: new Abstract: Audio language models (ALMs) are increasingly used for speech-based understanding, yet their ability to perform semantic reasoning beyond transcription, Text-to-Audio Retrieval, Captioning, and Question-Answering accuracy remains insufficiently benchmarked.

Reading status

Priority

About this paper

Authors: Chibuzor Okocha, Christan Grant
Released: 2026-06-11 (28d ago)
Topic: Related to current project
Relation to my work: Read next because Afrispeech Semantics: Evaluating Audio Semantic Reasoning in Spoken Language Models Across Domains and Accents overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, under, eval, source, trained, language, model. Source: arxiv cs.CL (NLP).
arXiv: 2606.11219

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to Afrispeech Semantics: Evaluating Audio Semantic Reasoning in Spoken Language Models Across Domains and Accents. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.