Improving phoneme recognition of throat microphone speech recordings using transfer learning

Author:

Turan M.A. Tuğtekin,Erzin Engin

Funder

TÜBİTAK

Publisher

Elsevier BV

Subject

Computer Science Applications,Computer Vision and Pattern Recognition,Linguistics and Language,Language and Linguistics,Communication,Modeling and Simulation,Software

Reference34 articles.

1. Convolutional neural networks for speech recognition;Abdel-Hamid;IEEE Trans. Audio Speech Lang. Process.,2014

2. A comparison of features for speech, music discrimination;Carey,1999

3. Chen, M., Xu, Z., Weinberger, K.Q., Sha, F., 2012. Marginalized denoising autoencoders for domain adaptation. In: International Conference on Machine Learning. ICML, pp. 1627–1634.

4. A survey on heterogeneous transfer learning;Day;J. Big Data,2017

5. Improved speech recognition in noisy environments by using a throat microphone;Dekens,2010

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2. Multi-Self-Supervised Learning Model-Based Throat Microphone Speech Recognition;2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC);2023-10-31

3. Using a novel in-mask non-invasive ventilator microphone to improve talker intelligibility in healthy and hospitalised adults;International Journal of Speech-Language Pathology;2023-10-13

4. Deep transfer learning for automatic speech recognition: Towards better generalization;Knowledge-Based Systems;2023-10

5. Evaluation on English Translator Speech Recognition System Based on Artificial Intelligence Algorithms;2023 International Conference on Network, Multimedia and Information Technology (NMITCON);2023-09-01

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