Hand Gesture Recognition for Sign Language Transcription

Sign Language is a language which allows mute people to communicate with other mute or non-mute people. The benefits provided by this language, however, disappear when one of the members of a group does not know Sign Language and a conversation starts using that language. In this document, I present a system that takes advantage of Convolutional Neural Networks to recognize hand letter and number gestures from American Sign Language based on depth images captured by the Kinect camera. In addition, as a byproduct of these research efforts, I collected a new dataset of depth images of American Sign Language letters and numbers, and I compared the presented method for image recognition against a similar dataset but for Vietnamese Sign Language. Finally, I present how this work supports my ideas for the future work on a complete system for Sign Language transcription

The Design and Application of an Acoustic Front-End for Use in Speech Interfaces

This thesis describes the design, implementation, and application of an acoustic front-end. Such front-ends constitute the core of automatic speech recognition systems. The front-end whose development is reported here has been designed for speaker-independent large vocabulary recognition. The emphasis of this thesis is more one of design than of application. This work exploits the current state-of-the-art in speech recognition research, for example, the use of Hidden Markov Models. It describes the steps taken to build a speaker-independent large vocabulary system from signal processing, through pattern matching, to language modelling. An acoustic front-end can be considered as a multi-stage process, each of which requires the specification of many parameters. Some parameters have fundamental consequences for the ultimate application of the front-end. Therefore, a major part of this thesis is concerned with their analysis and specification. Experiments were carried out to determine the characteristics of individual parameters, the results of which were then used to motivate particular parameter settings. The thesis concludes with some applications that point out, not only the power of the resulting acoustic front-end, but also its limitations

A cost, complexity and performance comparison of two automatic language identification architectures

This dissertation investigates the cost-complexity-performance relationship between two automatic language identification systems. The first is a state-of-the-art archi¬tecture, trained on about three hours of phonetically hand-labelled telephone speech obtained from the recognised OGLTS corpus. The second system, introduced by our¬selves, is a simpler design with a smaller, less complex parameter space. It is a vector quantisation-based approach which bears some resemblance to a system suggested by Sugiyama. Though trained on the same data, it has no need for any labels and is therefore less costly. A number of experiments are performed to find quasi-optimal parameters for the two systems. In further experiments the systems are evaluated and compared on a set of ten two-language tasks, spanning five languages. The more com¬plex system is shown to have a substantial performance advantage over the simpler design - 81% versus 65% on 40 seconds of speech. However, both results are well under reported state-of-the-art performance of 94% and would suggest that our systems can benefit from additional attention to implementation detail and optimisation of various parameters. Given the above, our suggested architecture may potentially provide an adequate solution where the high development cost associated with state-of-the-art technology and the necessary training corpora are prohibitive.Dissertation (M Eng (Computer Engineering))--University of Pretoria, 2006.Electrical, Electronic and Computer Engineeringunrestricte

Advances in Human-Robot Interaction

Rapid advances in the field of robotics have made it possible to use robots not just in industrial automation but also in entertainment, rehabilitation, and home service. Since robots will likely affect many aspects of human existence, fundamental questions of human-robot interaction must be formulated and, if at all possible, resolved. Some of these questions are addressed in this collection of papers by leading HRI researchers

Sentiment Analysis on YouTube Comments : Analysis of prevailing attitude towards Nokia Mobile Phones

The volume of textual data, more specifically, the magnitude of opinionated text on social media, has increased the interest of companies to closely analyze what their customers have to say about them and their products. This thesis explores the possibility of performing aspect-based sentiment analysis with YouTube comments. The comments on Nokia Mobile phones are the subject of the study in this thesis. First, manual labeling was performed to identify the aspect terms and sentiment and then categorize the aspects based on the aspect’s functionality on the phone. From the categorization, it was found out that people mainly have shown negative sentiment towards multiple aspects of the phone with maximum negative attitude towards the price of the phone. On the other hand, the only aspect that could gather a positive attitude was the phone’s-built quality. The result shows that there are multiple phone aspects that HMD Global can consider for current and future product improvement. Further, this study used the labeled data to perform supervised learning to classify the aspects and the aspect sentiment from the comments. With two features extraction techniques, BoW and TF-IDF, this paper has explored the performance of different machine learning models on YouTube comments. The models show good results for aspect classification; however, the model’s performance could be further improved for aspect sentiment classification. Overall, little attention to this area has been discussed because of the complexity, highly unstructured, and noisy nature of text on YouTube. However, despite the challenges, this platform can be valuable in producing insightful analysis, as presented in this thesis

Developing Deployable Spoken Language Translation Systems given Limited Resources

Approaches are presented that support the deployment of spoken language translation systems. Newly developed methods allow low cost portability to new language pairs. Proposed translation model pruning techniques achieve a high translation performance even in low memory situations. The named entity and specialty vocabulary coverage, particularly on small and mobile devices, is targeted to an individual user by translation model personalization

Rapid Generation of Pronunciation Dictionaries for new Domains and Languages

This dissertation presents innovative strategies and methods for the rapid generation of pronunciation dictionaries for new domains and languages. Depending on various conditions, solutions are proposed and developed. Starting from the straightforward scenario in which the target language is present in written form on the Internet and the mapping between speech and written language is close up to the difficult scenario in which no written form for the target language exists

The history and sociology of computer science and technology, collected vol. 4

A historiography and source material