Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.09270 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908959084904448 |
|---|---|
| author | Du, Yexing Liu, Kaiyuan Zhang, Bihe Pan, Youcheng Yang, Bo Huo, Liangyu Zhang, Xiyuan Xie, Jian He, Daojing Xiang, Yang Liu, Ming Qin, Bing |
| author_facet | Du, Yexing Liu, Kaiyuan Zhang, Bihe Pan, Youcheng Yang, Bo Huo, Liangyu Zhang, Xiyuan Xie, Jian He, Daojing Xiang, Yang Liu, Ming Qin, Bing |
| contents | With the rapid advancement of Multimodal Large Language Models (MLLMs), their potential has gained significant attention in Chinese Classical Studies (CCS). While existing research primarily focuses on text and visual modalities, the audio corpus within this domain remains largely underexplored. To bridge this gap, we introduce the Multi-task Classical Chinese Literary Genre Audio Corpus (MCGA), a 119-hour corpus comprising 22,000 audio samples. 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 MLLMs still face substantial challenges on the MCGA test set. Furthermore, we introduce a domain-specific metric for SEC and a metric to measure the consistency between speech and text capabilities. We release MCGA to the public to facilitate the development of more robust MLLMs. MCGA Corpus: https://github.com/yxduir/MCGA |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_09270 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MCGA: A Multi-task Classical Chinese Literary Genre Audio Corpus Du, Yexing Liu, Kaiyuan Zhang, Bihe Pan, Youcheng Yang, Bo Huo, Liangyu Zhang, Xiyuan Xie, Jian He, Daojing Xiang, Yang Liu, Ming Qin, Bing Computation and Language With the rapid advancement of Multimodal Large Language Models (MLLMs), their potential has gained significant attention in Chinese Classical Studies (CCS). While existing research primarily focuses on text and visual modalities, the audio corpus within this domain remains largely underexplored. To bridge this gap, we introduce the Multi-task Classical Chinese Literary Genre Audio Corpus (MCGA), a 119-hour corpus comprising 22,000 audio samples. 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 MLLMs still face substantial challenges on the MCGA test set. Furthermore, we introduce a domain-specific metric for SEC and a metric to measure the consistency between speech and text capabilities. We release MCGA to the public to facilitate the development of more robust MLLMs. MCGA Corpus: https://github.com/yxduir/MCGA |
| title | MCGA: A Multi-task Classical Chinese Literary Genre Audio Corpus |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2601.09270 |