This paper describes intensity and location normalization techniques for improving the performance of visual speech recognizers used in audio-visual speech recognition. For auditory speech recognition, there exist many methods for dealing with channel characteristics and speaker individualities, e.g., CMN (cepstral mean normalization), SAT (speaker adaptive training). We present two techniques ...

The reliability of Fingerprint recognition process in turn the Fingerprint Recognition system depends on the minutiae. Accurate minutiae depend on the quality of the fingerprint image. Most of the images are of poor quality. This leads to distorted levels of variation in grey-level values along the ridges and valleys. These variations are minimized by adapting normalization process, which has p...

While the authors of Batch Normalization (BN) identify and address an important problem involved in training deep networks– Internal Covariate Shift– the current solution has certain drawbacks. For instance, BN depends on batch statistics for layerwise input normalization during training which makes the estimates of mean and standard deviation of input (distribution) to hidden layers inaccurate...

Using a known speaker-intrinsic normalization procedure, formant data are scaled by the reciprocal of the geometric mean of the first three formant frequencies. This reduces the influence of the talker but results in a distorted vowel space. The proposed speaker-extrinsic procedure re-scales the normalized values by the mean formant values of vowels. When tested on the formant data of vowels pu...

We have developed a Bayesian classi er to determine whether syllables in connected Thai speech are weakly or strongly stressed by using ve acoustic parameters: syllable rhyme duration, mean F0, F0 standard deviation, mean energy, and the standard deviation of the energy. With speaker-dependent data normalization, we achieved a classi cation accuracy of 99%. The classi cation accuracy drops to 9...

In this work, we propose a frame selection scheme based on the smoothed instantaneous energy of samples, local order statistics for them, and average of a binary energy indicator over the frame to measure the reliability of frames. By selection of reliable frames, a four-stage feature normalization and transformation process is further proposed: mean normalization, variance normalization, first...

This paper re-examines the speech rhythm of Beijing Chinese and Pisa Italian by means of the Control/Compensation Index (CCI), with a view to normalizing the speech data, in order to reduce the effect of the rate factor. Two metrics were applied: (a) DnCCI, an adaptation to the CCI model of the nPVI normalization strategy; (b) SnCCI, a z-score normalization, which takes into account the actual ...

In the above paper,’ an alternative stability analysis was proposed for proving robust stability of model reference adaptive control (MRAC) that employs projection and static instead of dynamic normalization. It was shown in this paper that the analysis for the static normalization case follows directly from that used in the case of dynamic normalization using small in the mean rirguments. It w...

An intuitive normalization and gene identification method is proposed. After segmentation of the entire expression range into intensity intervals, the mean and standard deviation of the logarithm of expression ratios are calculated for each interval using the nearest neighbor genes. Genes with high differential expression are excluded from these calculations. For glass arrays, normalization is ...

We propose a new feature normalization scheme based on eigenspace, for achieving robust speech recognition. In particular, we employ the Mean and Variance Normalization (MVN) in eigenspace using unique and in– dependent eigenspaces to cepstra, delta and delta-delta cepstra respectively. We also normalize training data in eigenspace and get the model from the normalized training data. In additio...