Fetching the paper…
Reading the bibliography…
Recent end-to-end speech language models (SLMs) have expanded upon the capabilities of large language models (LLMs) by incorporating pre-trained speech models.
C. Busso, M. Bulut, C.-C. Lee, A. Kazemzadeh, E. Mower, S. Kim, J. N. Chang, S. Lee, and S. S. Narayanan, “Iemocap: Interactive emotional dyadic motion capture database,” Language resources and evaluation , vol. 42, pp. 335–359, 2008
2008
Earlier work this paper cites.
A. Nagrani, J. S. Chung, and A. Zisserman, “Voxceleb: A large-scale speaker identification dataset,” in Interspeech 2017 , 2017, pp. 2616–2620
2017
Earlier work this paper cites.
J. Yamagishi, C. Veaux, and K. MacDonald, “CSTR VCTK Corpus: English multi-speaker corpus for CSTR voice cloning toolkit (version 0.92),” 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
A. Ahamad, A. Anand, and P. Bhargava, “Accentdb: A database of non-native english accents to assist neural speech recognition,” in Proceedings of The 12th Language Resources and Evaluation Conference . Marseille, France: European Language Resources Association, May 2020, pp. 5353–5360. [Online]. Available: https://www.aclweb.org/anthology/2020.lrec-1.659
2020
Earlier work this paper cites.
T. Wolf, L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi, P. Cistac, T. Rault, R. Louf, M. Funtowicz et al. , “Transformers: State-of-the-art natural language processing,” in Proceedings of the 2020 conference on empirical methods in natural language processing: system demonstrations , 2020, pp. 38–45
2020
Earlier work this paper cites.
E. J. Hu, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, W. Chen et al. , “Lora: Low-rank adaptation of large language models,” in International Conference on Learning Representations , 2021
2021
Earlier work this paper cites.
J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou et al. , “Chain-of-thought prompting elicits reasoning in large language models,” Advances in neural information processing systems , vol. 35, pp. 24 824–24 837, 2022
2022
Earlier work this paper cites.
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
Y. Gong, A. H. Liu, H. Luo, L. Karlinsky, and J. Glass, “Joint audio and speech understanding,” in 2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) , 2023
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
R. Rafailov, A. Sharma, E. Mitchell, C. D. Manning, S. Ermon, and C. Finn, “Direct preference optimization: Your language model is secretly a reward model,” in Thirty-seventh Conference on Neural Information Processing Systems , 2023. [Online]. Available: https://openreview.net/forum?id=HPuSIXJaa9
Y. Gong, H. Luo, A. H. Liu, L. Karlinsky, and J. R. Glass, “Listen, think, and understand,” in The Twelfth International Conference on Learning Representations , 2024. [Online]. Available: https://openreview.net/forum?id=nBZBPXdJlC
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2023
Cited alongside, same era.
A. Radford, J. W. Kim, T. Xu, G. Brockman, C. McLeavey, and I. Sutskever, “Robust speech recognition via large-scale weak supervision,” in International Conference on Machine Learning . PMLR, 2023, pp. 28 492–28 518
2023
Cited alongside, same era.
J. Li, D. Li, S. Savarese, and S. Hoi, “BLIP-2: Bootstrapping language-image pre-training with frozen image encoders and large language models,” in Proceedings of the 40th International Conference on Machine Learning , 2023
2023
Cited alongside, same era.
K. Lee, K. Park, and D. Kim, “Dailytalk: Spoken dialogue dataset for conversational text-to-speech,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2023, pp. 1–5
2023
Cited alongside, same era.
Z. Guo, Y. Leng, Y. Wu, S. Zhao, and X. Tan, “Prompttts: Controllable text-to-speech with text descriptions,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2023, pp. 1–5
2023
Cited alongside, same era.
2023
Cited alongside, same era.
M. Lavechin, M. Métais, H. Titeux, A. Boissonnet, J. Copet, M. Rivière, E. Bergelson, A. Cristia, E. Dupoux, and H. Bredin, “Brouhaha: multi-task training for voice activity detection, speech-to-noise ratio, and c50 room acoustics estimation,” in 2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) . IEEE, 2023, pp. 1–7
2023
Cited alongside, same era.
2024
Cited alongside, same era.
2024
Cited alongside, same era.
R. Huang, M. Li, D. Yang, J. Shi, X. Chang, Z. Ye, Y. Wu, Z. Hong, J. Huang, J. Liu et al. , “Audiogpt: Understanding and generating speech, music, sound, and talking head,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 21, 2024, pp. 23 802–23 804
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
K.-H. Lu, Z. Chen, S.-W. Fu, H. Huang, B. Ginsburg, Y.-C. F. Wang, and H. yi Lee, “Desta: Enhancing speech language models through descriptive speech-text alignment,” in Interspeech 2024 , 2024, pp. 4159–4163
2024
Closest in time.
2024
Closest in time.
Y. Fathullah, C. Wu, E. Lakomkin, K. Li, J. Jia, Y. Shangguan, J. Mahadeokar, O. Kalinli, C. Fuegen, and M. Seltzer, “Audiochatllama: Towards general-purpose speech abilities for llms,” in Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) , 2024, pp. 5522–5532
2024
Closest in time.