Abstract
In building speech recognition based applications, robustness to different
noisy background condition is an important challenge. In this paper
bimodal approach is proposed to improve the robustness of Hindi speech
recognition system. Also an importance of different types of visual
features is studied for audio visual automatic speech recognition (AVASR)
system under diverse noisy audio conditions. Four sets of visual feature
based on Two-Dimensional Discrete Cosine Transform feature (2D-DCT),
Principal Component Analysis (PCA), Two-Dimensional Discrete Wavelet
Transform followed by DCT (2D-DWT- DCT) and Two-Dimensional Discrete
Wavelet Transform followed by PCA (2D-DWT-PCA) are reported. The audio
features are extracted using Mel Frequency Cepstral coefficients (MFCC)
followed by static and dynamic feature. Overall, 48 features, i.e. 39
audio features and 9 visual features are used for measuring the
performance of the AVASR system. Also, the performance of the AVASR using
noisy speech signal generated by using NOISEX database is evaluated for
different Signal to Noise ratio (SNR: 30 dB to −10 dB) using Aligarh
Muslim University Audio Visual (AMUAV) Hindi corpus. AMUAV corpus is Hindi
continuous speech high quality audio visual databases of Hindi sentences
spoken by different subjects.
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