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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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Authors and Affiliations

Prashant Upadhyaya
Omar Farooq
M.R. Abidi
Priyanka Varshney
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Abstract

Any industrial process needs to work with the optimal operating conditions and thus the evaluation of their robustness is a critical issue. A modeling of a laboratoryscale wire-to-plane two stages electrostatic precipitator for guiding the identification of the set point, is presented this in paper. The procedure consists of formulating recommendations regarding the choice of optimal values for electrostatic precipitation. A twostages laboratory precipitator was used to carry out the experiments, with samples of wood particles of average granulometric size 10 μm. The parameters considered in the present study are the negative applied high voltage of the ionization stage, the positive voltage of the collection stage and the air speed. First, three “one-factor-at-a-time” experiments were performed followed by a factorial composite design experiments, based on a two-step strategy: 1) identify the domain of variation of the variables; 2) set point identification and optimization of the process.
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Authors and Affiliations

Djelloul Berrached
Amar Tilmatine
Farid Miloua
Malika Bengrit

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