Bottleneck Feature Extraction in Punjabi Adult Speech Recognition System

Author:

Bala Shashi,Kadyan Virender,Bhardwaj Vivek

Publisher

Springer Singapore

Reference20 articles.

1. Seltzer, M. L., Yu, D., Wang, Y.: An investigation of deep neural networks for noise robust speech recognition. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 7398–7402 (2013)

2. Patel, I., Rao, Y.S.: Speech recognition using hmm with mfcc-an analysis using frequency specral decomposion technique. Sig. Image Process. Int. J. (SIPIJ) 1(2), 101–110 (2010)

3. Kadyan, V., Mantri, A., Aggarwal, R.K.: Improved filter bank on multitaper framework for robust Punjabi-ASR system. Int. J. Speech Technol. 1–14 (2019)

4. Yu, D., Seltzer, M.L.: Improved bottleneck features using pretrained deep neural networks. In: Twelfth Annual Conference of the International Speech Communication Association (2011)

5. Grézl, F., Karafiat, M., Burget, L.: Investigation into bottle-neck features for meeting speech recognition. In: Tenth Annual Conference of the International Speech Communication Association (2009)

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