PhonHuBERT: A Phoneme Transcription Tool for Song Datasets
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-4399-5_12
Reference17 articles.
1. Hsu, W.N., Bolte, B., Tsai, Y.H.H., Lakhotia, K., Salakhutdinov, R., Mohamed, A.: HuBERT: self-supervised speech representation learning by masked prediction of hidden units. IEEE/ACM Trans. Audio Speech Lang. Process. 29, 3451–3460 (2021)
2. Li, J., et al.: Recent advances in end-to-end automatic speech recognition. APSIPA Trans. Signal Inf. Process. 11(1) (2022)
3. Prabhavalkar, R., Hori, T., Sainath, T.N., Schlüter, R., Watanabe, S.: End-to-end speech recognition: a survey. IEEE/ACM Trans. Audio Speech Lang. Process. 32, 325–351 (2024)
4. Zeyer, A., Bahar, P., Irie, K., Schlüter, R., Ney, H.: A comparison of transformer and LSTM encoder decoder models for ASR. In: 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), pp. 8–15 (2019)
5. Li, J., Wu, Y., Gaur, Y., Wang, C., Zhao, R., Liu, S.: On the comparison of popular end-to-end models for large scale speech recognition. In: Interspeech 2020 (2020)
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