Details

Title

Comparative Study of Visual Feature for Bimodal Hindi Speech Recognition

Journal title

Archives of Acoustics

Yearbook

2015

Numer

No 4

Publication authors

Divisions of PAS

Nauki Techniczne

Publisher

Committee on Acoustics PAS, PAS Institute of Fundamental Technological Research, Polish Acoustical Society

Date

2015[2015.01.01 AD - 2015.12.31 AD]

Identifier

ISSN 0137-5075 ; eISSN 2300-262X

References

Jürgens (2013), The robustness of speech representations obtained from simulated auditory nerve fibers under different noise conditions JASA Express Letters of the Acoustical Society of America, Journal, 134, 282. ; Huang (2004), Audio - visual speech recognition using an infrared headset, Speech Communication, 44, 83, doi.org/10.1016/j.specom.2004.10.007 ; Pradhan (2012), Speaker verification in sensor and acoustic environment mismatch conditions of Speech Technology, International Journal, 15, 381. ; Lokesh (2012), Robust Speech Feature Prediction Using Mel - LPC to Improve Recognition Accuracy Information Technology, Journal, 11, 1644. ; Hansen (2009), Analysis of CFA - BF : Novel combined fixed / adaptive beamforming for robust speech recognition in real car environments, Speech Communication, 52, 134, doi.org/10.1016/j.specom.2009.09.001 ; Lee (2008), Robust audio visual speech recognition based on late integration Transactions on Multimedia August, IEEE, 10, 767. ; Zhou (2014), A compact representation of visual speech data using latent variables Transactions on Pattern Analysis and Machine Intelligence, IEEE, 36, 181. ; Chourasia (2007), Hindi Speech Recognition under Noisy Conditions of Acoustic Society India pp, International Journal, 41. ; Farooq (2010), Wavelet sub - band based temporal features for robust Hindi phoneme recognition on Wavelets and Multiresolution Information Processing, International Journal, 8, 847. ; Mishra (2011), Robust Features for Connected Hindi Digits Recognition of Signal Processing Image Processing and Pattern Recognition, International Journal, 4, 79. ; Varga (1993), Assessment for automatic speech recognition : II NOISEX database and an experiment to study the effect of additive noise on speech recognition systems, Speech Communication, 12, 247, doi.org/10.1016/0167-6393(93)90095-3 ; Potamianos (2003), Recent Advances in the Automatic Recognition of Audio visual Speech Invite paper Proceedings of the, IEEE, 91, 1306, doi.org/10.1109/JPROC.2003.817150 ; Varshney (2014), Hindi viseme recognition using subspace DCT features of Applied Pattern Recognition, International Journal, 1. ; Bruce (2002), Dimensionality Reduction of Hyperspectral Data Using Discrete Wavelet Transform Feature Extraction Transactions on Geoscience and Remote Sensing, IEEE, 40, 2331. ; Navnath (2012), DWT and LPC based feature extraction methods for isolated word recognition on Audio Speech and Music Processing pp, EURASIP Journal, 1. ; Chen (2001), Audio visual speech processing Signal Processing Magazine pp, IEEE, 9. ; Neti (2002), A Large Vocabulary Continuous Speech Recognition System For Hindi Proceeding of Works Signal Process pp, Multimedia, 475. ; Naomi (2015), TCD - TIMIT : An AudioVisual Corpus of Continuous Speech on Multimedia, IEEE Transactions, 17, 603.

DOI

10.1515/aoa-2015-0061

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