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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.11006 |
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| _version_ | 1866917080073240576 |
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| author | Liu, HongYu Wan, Ruijie Han, Yueju Li, Junxin Lu, Liuxing He, Chao Cai, Lihua |
| author_facet | Liu, HongYu Wan, Ruijie Han, Yueju Li, Junxin Lu, Liuxing He, Chao Cai, Lihua |
| contents | Audio classification plays an essential role in sentiment analysis and emotion recognition, especially for analyzing customer attitudes in marketing phone calls. Efficiently categorizing customer purchasing propensity from large volumes of audio data remains challenging. In this work, we propose a novel Multi-Segment Multi-Task Fusion Network (MSMT-FN) that is uniquely designed for addressing this business demand. Evaluations conducted on our proprietary MarketCalls dataset, as well as established benchmarks (CMU-MOSI, CMU-MOSEI, and MELD), show MSMT-FN consistently outperforms or matches state-of-the-art methods. Additionally, our newly curated MarketCalls dataset will be available upon request, and the code base is made accessible at GitHub Repository MSMT-FN, to facilitate further research and advancements in audio classification domain. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_11006 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MSMT-FN: Multi-segment Multi-task Fusion Network for Marketing Audio Classification Liu, HongYu Wan, Ruijie Han, Yueju Li, Junxin Lu, Liuxing He, Chao Cai, Lihua Sound Artificial Intelligence Audio classification plays an essential role in sentiment analysis and emotion recognition, especially for analyzing customer attitudes in marketing phone calls. Efficiently categorizing customer purchasing propensity from large volumes of audio data remains challenging. In this work, we propose a novel Multi-Segment Multi-Task Fusion Network (MSMT-FN) that is uniquely designed for addressing this business demand. Evaluations conducted on our proprietary MarketCalls dataset, as well as established benchmarks (CMU-MOSI, CMU-MOSEI, and MELD), show MSMT-FN consistently outperforms or matches state-of-the-art methods. Additionally, our newly curated MarketCalls dataset will be available upon request, and the code base is made accessible at GitHub Repository MSMT-FN, to facilitate further research and advancements in audio classification domain. |
| title | MSMT-FN: Multi-segment Multi-task Fusion Network for Marketing Audio Classification |
| topic | Sound Artificial Intelligence |
| url | https://arxiv.org/abs/2511.11006 |