dorsal/arxiv
View SchemaMCGA: A Multi-task Classical Chinese Literary Genre Audio Corpus
| Authors | Yexing Du, Kaiyuan Liu, Bihe Zhang, Youcheng Pan, Bo Yang, Liangyu Huo, Xiyuan Zhang, Jian Xie, Daojing He, Yang Xiang, Ming Liu, Bin Qin |
|---|---|
| Categories | |
| ArXiv ID | 2601.09270vv2 |
| URL | https://arxiv.org/abs/2601.09270 |
| License | http://creativecommons.org/licenses/by-nc-sa/4.0/ |
Abstract
With the rapid advancement of Multimodal Large Language Models (MLLMs), their potential has garnered significant attention in Chinese Classical Studies (CCS). While existing research has primarily focused on text and visual modalities, the audio corpus within this domain remains largely underexplored. To bridge this gap, we propose the Multi-task Classical Chinese Literary Genre Audio Corpus (MCGA). It encompasses a diverse range of literary genres across six tasks: Automatic Speech Recognition (ASR), Speech-to-Text Translation (S2TT), Speech Emotion Captioning (SEC), Spoken Question Answering (SQA), Speech Understanding (SU), and Speech Reasoning (SR). Through the evaluation of ten MLLMs, our experimental results demonstrate that current models still face substantial challenges when processed on the MCGA test set. Furthermore, we introduce an evaluation metric for SEC and a metric to measure the consistency between the speech and text capabilities of MLLMs. We release MCGA and our code to the public to facilitate the development of MLLMs with more robust multidimensional audio capabilities in CCS. MCGA Corpus: https://github.com/yxduir/MCGA
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"abstract": "With the rapid advancement of Multimodal Large Language Models (MLLMs), their potential has garnered significant attention in Chinese Classical Studies (CCS). While existing research has primarily focused on text and visual modalities, the audio corpus within this domain remains largely underexplored. To bridge this gap, we propose the Multi-task Classical Chinese Literary Genre Audio Corpus (MCGA). It encompasses a diverse range of literary genres across six tasks: Automatic Speech Recognition (ASR), Speech-to-Text Translation (S2TT), Speech Emotion Captioning (SEC), Spoken Question Answering (SQA), Speech Understanding (SU), and Speech Reasoning (SR). Through the evaluation of ten MLLMs, our experimental results demonstrate that current models still face substantial challenges when processed on the MCGA test set. Furthermore, we introduce an evaluation metric for SEC and a metric to measure the consistency between the speech and text capabilities of MLLMs. We release MCGA and our code to the public to facilitate the development of MLLMs with more robust multidimensional audio capabilities in CCS. MCGA Corpus: https://github.com/yxduir/MCGA",
"arxiv_id": "2601.09270",
"authors": [
"Yexing Du",
"Kaiyuan Liu",
"Bihe Zhang",
"Youcheng Pan",
"Bo Yang",
"Liangyu Huo",
"Xiyuan Zhang",
"Jian Xie",
"Daojing He",
"Yang Xiang",
"Ming Liu",
"Bin Qin"
],
"categories": [
"cs.CL"
],
"license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
"title": "MCGA: A Multi-task Classical Chinese Literary Genre Audio Corpus",
"url": "https://arxiv.org/abs/2601.09270",
"version": "v2"
},
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"variant": "snapshot-2026-01-17",
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