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008			191107t20182018my a|||fram|| 000 0 eng d
020			_aTHE0008399(Local) _qhardback
040			_aUMP _beng _cUMP _erda
090			_aFKEE .T48 2018 r Thesis
100	1		_aThum, Wei Seong, _eauthor.
245	1	0	_aDevelopment on SNR estimator for audio-visual speech recognition based on waveform amplitude distribution analysis / _cThum Wei Seong
264		1	_aKuantan, Pahang : _bUMP, _c2018
264		4	_c© 2018
300			_axiii, 80 pages : _billustrations (some color) ; _c30 cm. + _e1 CD-ROM
336			_atext _2rdacontent
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
337			_acomputer _2rdamedia
338			_avolume _2rdacarrier
338			_acomputer disc _2rdacarrier
347			_atext file _bPDF _2rda
500			_aFaculty of Electrical and Electronics Engineering
502			_aThesis (Master of Science) -- Universiti Malaysia Pahang – 2018
504			_aIncludes bibliographical references
520	3		_aFor audio-visual speech recognition (AVSR) that uses audio modality combined with visual modality, the performance of speech recognition system can be improved, particularly when operating in a noisy environment. Audio modality can be easily corrupted by ambient noise, and this causes difficulty in distinguishing the actual speech signal with noise signal correctly. Signal-to-noise ratio (SNR) is a fundamental measuring ratio of signal power over noise power, which is expressed in decibels (dB). One of the most famous SNR estimation techniques is the waveform amplitude distribution analysis (WADA), where it assumes that the amplitude of speech and noise follows gamma and Gaussian distributions. It has been used in some research works as a benchmark for result comparison. However, there is no clear instruction on how to build the look-up table. In this work, the development and rebuild of the look-up table using the own database corrupted with general white noise as the noise reference has been proposed. The reconstruction of WADA look-up table technique, which is known as the waveform amplitude distribution analysis-white (WADA-W), is able to enhance the SNR estimation by referring to the reconstructed WADA-W look-up table instead of a general WADA precomputed look-up table. The proposed WADA-W SNR estimation technique was evaluated by developing an AVSR system that utilised mel-frequency cepstral coefficients (MFCC) features and shape-based visual features from two speech databases: LUNA-V and CUAVE. According to the experimental result, it showed that by referring to the WADA-W look-up table, it is capable of performing a consistent SNR estimation with more accurate and less bias result compared to the original WADA technique under four types of noises, which are white, babble, factory1, and factory2 noises from the NOISEX-92 dataset. The overall deviation of the SNR estimation of the LUNA-V database using the proposed WADA-W technique was just approximately 9.6dB, whereas the deviation of NIST and WADA techniques was approximately 42.3dB and 67.3dB respectively. By using the same proposed technique for CUAVE database, the overall deviation of the SNR estimation was only 13.3dB, whereas the deviation of NIST and WADA techniques was 50.6dB and 62.3dB respectively. The classification was done using the multi-stream hidden Markov model (MSHMM) with leave-one-out cross-validation (LOOCV) technique. From the experiments, it showed that the proposed AVSR system able to achieve the highest accuracy at 96.6% using LUNA-V database and 95.2% for CUAVE database under clean condition. In conclusion, the proposed WADA-W SNR estimator able to improve by 4.5% and 12.7% compared to the original WADA technique by using the LUNA-V and CUAVE database respectively.
610	2	0	_aFaculty of Electrical and Electronics Engineering _xDissertations
650		0	_aUniversities and colleges _xDisertations
650		0	_aTheses
942			_2lcc _cTHESIS