[1] Akinpelu, S., Viriri, S., Haroon Yousaf, M.: SwintSer: an improved bilingual speech emotion recognition using shift window transformer. Cogn. Comput. 17(4), 129 (2025)

[2] Aysa, Z., Ablimit, M., Yilahun, H., Hamdulla, A.: Language identification-based evaluation of single-channel speech separation of overlapped speeches. Information 13(10), 492 (2022)

[3] Borsdorf, M., Scheck, K., Li, H., Schultz, T.: Blind language separation: disentangling multilingual cocktail party voices by language. In: INTERSPEECH, pp. 256–260 (2022)

[4] Chen, J., Mao, J., Liu, D.: Dual-path transformer network: direct context-aware modeling for end-to-end monaural speech separation. In: INTERSPEECH (2020)

[5] Chen, Z., Luo, Y., Mesgarani, N.: Deep attractor network for single-microphone speaker separation. In: International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 246–250. IEEE (2017)

[6] Cosentino, J., Pariente, M., Cornell, S., Deleforge, A., Vincent, E.: Librimix: an open-source dataset for generalizable speech separation (2020)

[7] Kolbæk, M., Yu, D., Tan, Z.H., Jensen, J.: Multitalker speech separation with utterance-level permutation invariant training of deep recurrent neural networks. IEEE/ACM Trans. Audio Speech Lang. Process. 25(10), 1901–1913 (2017)

[8] Luo, Y., Mesgarani, N.: Conv-TASNET: surpassing ideal time-frequency magnitude masking for speech separation. IEEE/ACM Trans. Audio Speech Lang. Process. 27(8), 1256–1266 (2019)

[9] Luo, Y., Chen, Z., Yoshioka, T.: Dual-path RNN: efficient long sequence modeling for time-domain single-channel speech separation. In: International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 46–50. IEEE (2020)

[10] Luo, Y., Mesgarani, N.: TASNet: time-domain audio separation network for real-time, single-channel speech separation. In: International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 696–700. IEEE (2018)

[11] Minh Tan, H., Fhadli, M., Kim Quoc, N., Vu, D.Q.: Pre-trained self-attention framework: an efficient mechanism for source separation. In: Conference on Information Technology and its Applications, pp. 99–110. Springer (2024). https://doi.org/10.1007/978-3-031-74127-2_9

[12] Subakan, C., Ravanelli, M., Cornell, S., Bronzi, M., Zhong, J.: Attention is all you need in speech separation. In: International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 21–25. IEEE (2021)

[13] Tan, H.M., Vu, D.Q., Lee, C.T., Li, Y.H., Wang, J.C.: Selective mutual learning: an efficient approach for single channel speech separation. In: International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3678–3682. IEEE (2022)

[14] Tan, H.M., Vu, D.Q., Wang, J.C.: Selinet: a lightweight model for single channel speech separation. In: ICASSP 2023–2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5. IEEE (2023)

[15] Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

[16] VinBigData: Vinbigdata-vlsp2020-100h: a Vietnamese speech corpus for ASR. https://institute.vinbigdata.org (2020). 100h corpus, reading + spontaneous speech, speaker gender and dialect metadata

[17] Vincent, E., Gribonval, R., Févotte, C.: Performance measurement in blind audio source separation. Trans. Audio, Speech Lang. Process. 14(4), 1462–1469 (2006)

[18] Yan, B., et al.: Joint modeling of code-switched and monolingual ASR via conditional factorization. In: ICASSP 2022–2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6412–6416. IEEE (2022)

[19] Zhao, S., Ma, B.: MossFormer: pushing the performance limit of monaural speech separation using gated single-head transformer with convolution-augmented joint self-attentions. In: International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5. IEEE (2023)

[20] Zhao, S., et al.: MossFormer2: combining transformer and RNN-free recurrent network for enhanced time-domain monaural speech separation. In: International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 10356–10360. IEEE (2024)

[21] Zhao, S., Nguyen, T.H., Wang, H., Ma, B.: Towards natural bilingual and code-switched speech synthesis based on mix of monolingual recordings and cross-lingual voice conversion. In: Proceedings of the Interspeech 2020, pp. 2927–2931 (2020)