The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
Multitalker Speech Separation With Utterance-Level Permutation Invariant Training of Deep Recurrent Neural Networks
Open accessAbstract
In this paper, we propose the utterance-level permutation invariant training (uPIT) technique. uPIT is a practically applicable, end-to-end, deep-learning-based solution for speaker independent multitalker speech separation. Specifically, uPIT extends the recently proposed permutation invariant training (PIT) technique with an utterance-level cost function, hence eliminating the need for solving an additional permutation problem during inference, which is otherwise required by frame-level PIT. We achieve this using recurrent neural networks (RNNs) that, during training, minimize the utterance-level separation error, hence forcing separated frames belonging to the same speaker to be aligned to the same output stream. In practice, this allows RNNs, trained with uPIT, to separate multitalker mixed speech without any prior knowledge of signal duration, number of speakers, speaker identity, or gender. We evaluated uPIT on the WSJ0 and Danish two- and three-talker mixed-speech separation tasks and found that uPIT outperforms techniques based on nonnegative matrix factorization and computational auditory scene analysis, and compares favorably with deep clustering, and the deep attractor network. Furthermore, we found that models trained with uPIT generalize well to unseen speakers and languages. Finally, we found that a single model, trained with uPIT, can handle both two-speaker, and three-speaker speech mixtures.
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A review of deep learning techniques for speech processing
Mehrish, A., Majumder, N., Bharadwaj, R., Mihalcea, R., & Poria, S. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101869 · Cited by 389
Kinoshita, K., Ochiai, T., Delcroix, M., & Nakatani, T. (2020) · ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp40776.2020.9053266 · Cited by 104
Metadata
- Authors
- Kolbaek, M., Yu, D., Tan, Z.-H., & Jensen, J.
- Venue
- IEEE/ACM Transactions on Audio, Speech, and Language Processing
- Published
- 2017-10
- Volume
- 25
- Issue
- 10
- Pages
- 1901-1913
- ISSN
- 2329-9290 (ISSN-L)
- 2329-9304
- OpenAlex
- W2734774145
- Cited by
- 820
- References
- 45
- FWCI
- 66.08
- Type
- journal article
- Language
- en
- Open access
- green
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- Kolbaek, M., Yu, D., Tan, Z.-H., & Jensen, J. (2017). Multitalker Speech Separation With Utterance-Level Permutation Invariant Training of Deep Recurrent Neural Networks. IEEE/ACM Transactions on Audio, Speech, and Language Processing. https://doi.org/10.1109/taslp.2017.2726762
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