PERFORMANCE OF DIFFERENT CLASSIFIERS IN SPEECH RECOGNITION

Speech is the most natural means of communication among human beings and speech processing and recognition are intensive areas of research for the last five decades. Since speech recognition is a pattern recognition problem, classification is an important part of any speech recognition system. In this work, a speech recognition system is developed for recognizing speaker independent spoken digits in Malayalam. Voice signals are sampled directly from the microphone. The proposed method is implemented for 1000 speakers uttering 10 digits each. Since the speech signals are affected by background noise, the signals are tuned by removing the noise from it using wavelet denoising method based on Soft Thresholding. Here, the features from the signals are extracted using Discrete Wavelet Transforms (DWT) because they are well suitable for processing non-stationary signals like speech. This is due to their multi- resolutional, multi-scale analysis characteristics. Speech recognition is a multiclass classification problem. So, the feature vector set obtained are classified using three classifiers namely, Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Naive Bayes classifiers which are capable of handling multiclasses. During classification stage, the input feature vector data is trained using information relating to known patterns and then they are tested using the test data set. The performances of all these classifiers are evaluated based on recognition accuracy. All the three methods produced good recognition accuracy. DWT and ANN produced a recognition accuracy of 89%, SVM and DWT combination produced an accuracy of 86.6% and Naive Bayes and DWT combination produced an accuracy of 83.5%. ANN is found to be better among the three methods.

Attentional Selection in Object Recognition

A key problem in object recognition is selection, namely, the problem of identifying regions in an image within which to start the recognition process, ideally by isolating regions that are likely to come from a single object. Such a selection mechanism has been found to be crucial in reducing the combinatorial search involved in the matching stage of object recognition. Even though selection is of help in recognition, it has largely remained unsolved because of the difficulty in isolating regions belonging to objects under complex imaging conditions involving occlusions, changing illumination, and object appearances. This thesis presents a novel approach to the selection problem by proposing a computational model of visual attentional selection as a paradigm for selection in recognition. In particular, it proposes two modes of attentional selection, namely, attracted and pay attention modes as being appropriate for data and model-driven selection in recognition. An implementation of this model has led to new ways of extracting color, texture and line group information in images, and their subsequent use in isolating areas of the scene likely to contain the model object. Among the specific results in this thesis are: a method of specifying color by perceptual color categories for fast color region segmentation and color-based localization of objects, and a result showing that the recognition of texture patterns on model objects is possible under changes in orientation and occlusions without detailed segmentation. The thesis also presents an evaluation of the proposed model by integrating with a 3D from 2D object recognition system and recording the improvement in performance. These results indicate that attentional selection can significantly overcome the computational bottleneck in object recognition, both due to a reduction in the number of features, and due to a reduction in the number of matches during recognition using the information derived during selection. Finally, these studies have revealed a surprising use of selection, namely, in the partial solution of the pose of a 3D object.

An Analysis of the Effect of Gaussian Error in Object Recognition

Object recognition is complicated by clutter, occlusion, and sensor error. Since pose hypotheses are based on image feature locations, these effects can lead to false negatives and positives. In a typical recognition algorithm, pose hypotheses are tested against the image, and a score is assigned to each hypothesis. We use a statistical model to determine the score distribution associated with correct and incorrect pose hypotheses, and use binary hypothesis testing techniques to distinguish between them. Using this approach we can compare algorithms and noise models, and automatically choose values for internal system thresholds to minimize the probability of making a mistake.

Translation Invariance in Object Recognition, and Its Relation to Other Visual Transformations

Human object recognition is generally considered to tolerate changes of the stimulus position in the visual field. A number of recent studies, however, have cast doubt on the completeness of translation invariance. In a new series of experiments we tried to investigate whether positional specificity of short-term memory is a general property of visual perception. We tested same/different discrimination of computer graphics models that were displayed at the same or at different locations of the visual field, and found complete translation invariance, regardless of the similarity of the animals and irrespective of direction and size of the displacement (Exp. 1 and 2). Decisions were strongly biased towards same decisions if stimuli appeared at a constant location, while after translation subjects displayed a tendency towards different decisions. Even if the spatial order of animal limbs was randomized ("scrambled animals"), no deteriorating effect of shifts in the field of view could be detected (Exp. 3). However, if the influence of single features was reduced (Exp. 4 and 5) small but significant effects of translation could be obtained. Under conditions that do not reveal an influence of translation, rotation in depth strongly interferes with recognition (Exp. 6). Changes of stimulus size did not reduce performance (Exp. 7). Tolerance to these object transformations seems to rely on different brain mechanisms, with translation and scale invariance being achieved in principle, while rotation invariance is not.

Role of color in face recognition

One of the key challenges in face perception lies in determining the contribution of different cues to face identification. In this study, we focus on the role of color cues. Although color appears to be a salient attribute of faces, past research has suggested that it confers little recognition advantage for identifying people. Here we report experimental results suggesting that color cues do play a role in face recognition and their contribution becomes evident when shape cues are degraded. Under such conditions, recognition performance with color images is significantly better than that with grayscale images. Our experimental results also indicate that the contribution of color may lie not so much in providing diagnostic cues to identity as in aiding low-level image-analysis processes such as segmentation.

Tools to use when writing a thesis in Word 2010/2013 (PC)

This collection of videos shows you how to use a range of time-saving tools when writing a thesis in MS Word 2010/2013. See the full SupportGuide at http://www.go.soton.ac.uk/thesispc. There are videos on using styles; creating tables of contents and tables of figures; using the Navigation Pane; using the Browse Object tool and many more. There is an equivelent collection for use with Word 2011 which is for use with Apple computers.

Using Cross-references in Word 2010 (PC)

Cross-references allow you to tell the reader where they can find more detailed content on a subject within in your document. This video shows you how to set up, navigate with and refresh cross-references in Word 2010. Download the video for the best viewing experience.

Using Cross-referencing in Word 2011 (Mac)

Cross-references allow you to tell the reader where they can find more detailed content on a subject within in your document. This video shows you how to set up, navigate with and refresh cross-references in Word 2011. Download the video for the best viewing experience.

Adding an EndNote reference list in the Reference section of a thesis in Word 2010 (PC)

This video shows how to get your list of EndNote references in to the Reference section of the University template. EndNote wants to place the list at the very end of the document, but in the University's thesis structure the References are followed by the Bibliography. This video shows how to deal with this issue.

How do I get the Chapter number in Table or Figure captions to restart in Appendices using the University thesis template in Word 2010?

If you have added the Chapter number to your Table or Figure captions they will show as 1.1, 1.2 and so on. This is linked to the numbering used in the Heading 1 style. However, once you get to the Appendices the last Chapter number will continue throughout the Appendices as the Appendix heading isn't Heading 1. So what you need to do is get Word to understand that the style from which it should be picking up the first part of the Caption has changed and that it will need to restart the numbering again in each subsequent Appendix. This isn't too complex but you must follow the instructions to the letter or else it won't work.

How do I get the Chapter number in Table or Figure captions to restart in Appendices using the University thesis template in Word 2011?

If you have added the Chapter number to your Table or Figure captions they will show as 1.1, 1.2 and so on. This is linked to the numbering used in the Heading 1 style. However, once you get to the Appendices the last Chapter number will continue throughout the Appendices as the Appendix heading isn't Heading 1.

Attaching numbering to headings in Word 2010

In a long document such as a thesis you'll often want to attach a numbering system to your headings. This will get Word to automatically apply numbering as you add and removing headings from the document. You'll also be able to use them for cross-referencing purposes.

Using the Table of Authorities functionality to make a Glossary in Word 2010

Word does not contain a Glossary creation tool, you may find it easiest to just create a the list yourself manually. If you want to use Word then the only tool that can be co-opted to help is the Table of Authorities and this document sets out step by step instructions on how to do this.

Using the Table of Authorities functionality to make a Glossary in Word 2011

Word does not contain a Glossary creation tool, you may find it easiest to just create a the list yourself manually. If you want to use Word then the only tool that can be co-opted to help is the Table of Authorities and this document sets out step by step instructions on how to do this in Word 2011.

Gender differences among undergraduates in the recognition of emotional facial expressions

The perceptive accuracy of university students was compared between men and women, from sciences and humanities courses, to recognize emotional facial expressions. emotional expressions have had increased interest in several areas involved with human interaction, reflecting the importance of perceptive skills in human expression of emotions for the effectiveness of communication. Two tests were taken: one was a quick exposure (0.5 s) of 12 faces with an emotional expression, followed by a neutral face. subjects had to tell if happiness, sadness, anger, fear, disgust or surprise was flashed, and each emotion was shown twice, at random. on the second test 15 faces with the combination of two emotional expressions were shown without a time limit, and the subject had to name one of the emotions of the previous list. in this study, women perceived sad expressions better while men realized more happy faces. there was no significant difference in other emotions detection like anger, fear, surprise, disgust. Students of humanities and sciences areas of both sexes, when compared, had similar capacities to perceive emotional expressions