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English Information

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Authors
# Name
1 Yanna Gonçalves(yannatorres@alu.ufc.br)
2 Vincenzo Fadda(vincenzo@alu.ufc.br)
3 Ticiana Coelho da Silva(ticianalc@insightlab.ufc.br)
4 José Macêdo(jose.macedo@insightlab.ufc.br)

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Reference
# Reference
1 Abouelenin, A., Ashfaq, A., Atkinson, A., Awadalla, H., Bach, N., Bao, J., Benhaim, A., Cai, M., Chaudhary, V., Chen, C., et al. (2025). Phi-4-mini technical report: Compact yet powerful multimodal language models via mixture-of-loras. arXiv preprint arXiv:2503.01743.
2 Goldstein, J. and Carbonell, J. G. (1998). Summarization:(1) using mmr for diversity-based reranking and (2) evaluating summaries. In TIPSTER TEXT PROGRAM PHASE III: Proceedings of a Workshop held at Baltimore, Maryland, October 13-15, 1998, pages 181–195.
3 Ma, R., Qian, M., Gales, M., and Knill, K. (2025). Asr error correction using large language models. IEEE Transactions on Audio, Speech and Language Processing, 33:1389–1401.
4 Polevoi, A., Kragin, A., and Loukachevitch, N. (2025). Ground truth-free wer prediction for asr via audio quality and model confidence features. In International Conference on Speech and Computer, pages 29–44. Springer.
5 Radford, A., Kim, J. W., Xu, T., Brockman, G., McLeavey, C., and Sutskever, I. (2022). Robust speech recognition via large-scale weak supervision.
6 Reddy, C. K., Beyrami, E., Pool, J., Cutler, R., Srinivasan, S., and Gehrke, J. (2019). A scalable noisy speech dataset and online subjective test framework. In Interspeech 2019, pages 1816–1820. ISCA.
7 Sekoyan, M., Koluguri, N. R., Tadevosyan, N., Zelasko, P., Bartley, T., Karpov, N., Balam, J., and Ginsburg, B. (2025). Canary-1b-v2 & parakeet-tdt-0.6 b-v3: Efficient and high-performance models for multilingual asr and ast. arXiv preprint arXiv:2509.14128.
8 Srivastav, V., Zheng, S., Bezzam, E., Bihan, E. L., Koluguri, N., Żelasko, P., Majumdar, S., Moumen, A., and Gandhi, S. (2025). Open asr leaderboard: Towards reproducible and transparent multilingual and long-form speech recognition evaluation.
9 Wei, V. J., Wang, W., Jiang, D., Song, Y., and Wang, L. (2025). Asr-ec benchmark: Evaluating large language models on chinese asr error correction. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 1567–1575.
10 Yang, A., Li, A., Yang, B., Zhang, B., Hui, B., Zheng, B., Yu, B., Gao, C., Huang, C., Lv, C., Zheng, C., Liu, D., Zhou, F., Huang, F., Hu, F., Ge, H., Wei, H., Lin, H., Tang, J., Yang, J., Tu, J., Zhang, J., Yang, J., Yang, J., Zhou, J., Zhou, J., Lin, J., Dang, K., Bao, K., Yang, K., Yu, L., Deng, L., Li, M., Xue, M., Li, M., Zhang, P., Wang, P., Zhu, Q., Men, R., Gao, R., Liu, S., Luo, S., Li, T., Tang, T., Yin, W., Ren, X., Wang, X., Zhang, X., Ren, X., Fan, Y., Su, Y., Zhang, Y., Zhang, Y., Wan, Y., Liu, Y., Wang, Z., Cui, Z., Zhang, Z., Zhou, Z., and Qiu, Z. (2025). Qwen3 technical report.
11 Yang, C.-H. H., Gu, Y., Liu, Y.-C., Ghosh, S., Bulyko, I., and Stolcke, A. (2023). Generative speech recognition error correction with large language models and task-activating prompting. In 2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), pages 1–8. IEEE.
12 Yang, C.-H. H., Park, T., Gong, Y., Li, Y., Chen, Z., Lin, Y.-T., Chen, C., Hu, Y., Dhawan, K., Żelasko, P., et al. (2024). Large language model based generative error correction: A challenge and baselines for speech recognition, speaker tagging, and emotion recognition. In 2024 IEEE Spoken Language Technology Workshop (SLT), pages 371–378. IEEE.
13 Zhang, Y., Li, M., Long, D., Zhang, X., Lin, H., Yang, B., Xie, P., Yang, A., Liu, D., Lin, J., Huang, F., and Zhou, J. (2025). Qwen3 embedding: Advancing text embedding and reranking through foundation models. arXiv preprint arXiv:2506.05176.