Comparative Study of Visual Feature for Bimodal Hindi Speech Recognition

Journal title

Archives of Acoustics




vol. 40


No 4



Aligarh Muslim University audio visual corpus ; AVASR ; bimodal ; DCT ; DWT

Divisions of PAS

Nauki Techniczne




Polish Academy of Sciences, Institute of Fundamental Technological Research, Committee on Acoustics


2015[2015.01.01 AD - 2015.12.31 AD]


Artykuły / Articles


DOI: 10.1515/aoa-2015-0061


Archives of Acoustics; 2015; vol. 40; No 4; 609-619


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, ; 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, ; 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, ; Potamianos (2003), Recent Advances in the Automatic Recognition of Audio visual Speech Invite paper Proceedings of the, IEEE, 91, 1306, ; 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.