Spoken content retrieval: A survey of techniques and technologies

Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR

On the voice-activated question answering

[EN] Question answering (QA) is probably one of the most challenging tasks in the field of natural language processing. It requires search engines that are capable of extracting concise, precise fragments of text that contain an answer to a question posed by the user. The incorporation of voice interfaces to the QA systems adds a more natural and very appealing perspective for these systems. This paper provides a comprehensive description of current state-of-the-art voice-activated QA systems. Finally, the scenarios that will emerge from the introduction of speech recognition in QA will be discussed. © 2006 IEEE.This work was supported in part by Research Projects TIN2009-13391-C04-03 and TIN2008-06856-C05-02. This paper was recommended by Associate Editor V. Marik.Rosso, P.; Hurtado Oliver, LF.; Segarra Soriano, E.; Sanchís Arnal, E. (2012). On the voice-activated question answering. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews. 42(1):75-85. https://doi.org/10.1109/TSMCC.2010.2089620S758542

Testbot: A Chatbot-Based Interactive Interview Preparation Application

Chatbots are programs that mimic human conversation using Artificial Intelligence. It has become particularly popular to serve as an ultimate virtual assistant, helping one to complete tasks with answering questions and solving problems. In this paper, I designed an efficient and accurate chatbot, Testbot, which is integrated with a web application to help students prepare interviews for data science. To meet this usability requirement, I deployed and customized an open-source chatbot framework Rasa and set a form of action to keep track of the conversation. Testbot now supports basic functions such as responding to greetings and chitchat and more advanced features that could recognize interview requests, ask for user-generated keywords to retrieve questions from the database, score users’ answers, and send feedback. I demonstrated the application running on a local server with a user interface implemented.Master of Science in Information Scienc

Protectbot: A Chatbot to Protect Children on Gaming Platforms

Online gaming no longer has limited access, as it has become available to a high percentage of children in recent years. Consequently, children are exposed to multifaceted threats, such as cyberbullying, grooming, and sexting. The online gaming industry is taking concerted measures to create a safe environment for children to play and interact with, such efforts remain inadequate and fragmented. Different approaches utilizing machine learning (ML) techniques to detect child predatory behavior have been designed to provide potential detection and protection in this context. After analyzing the available AI tools and solutions it was observed that the available solutions are limited to the identification of predatory behavior in chat logs which is not enough to avert the multifaceted threats. In this thesis, we developed a chatbot Protectbot to interact with the suspect on the gaming platform. Protectbot leveraged the dialogue generative pre-trained transformer (DialoGPT) model which is based on Generative Pre-trained Transformer 2 (GPT-2). To analyze the suspect\u27s behavior, we developed a text classifier based on natural language processing that can classify the chats as predatory and non-predatory. The developed classifier is trained and tested on Pan 12 dataset. To convert the text into numerical vectors we utilized fastText. The best results are obtained by using non-linear SVM on sentence vectors obtained from fastText. We got a recall of 0.99 and an F_0.5-score of 0.99 which is better than the state-of-the-art methods. We also built a new dataset containing 71 predatory full chats retrieved from Perverted Justice. Using sentence vectors generated by fastText and KNN classifier, 66 chats out of 71 were correctly classified as predatory chats

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

An Investigation of Digital Reference Interviews: A Dialogue Act Approach

The rapid increase of computer-mediated communications (CMCs) in various forms such as micro-blogging (e.g. Twitter), online chatting (e.g. digital reference) and community- based question-answering services (e.g. Yahoo! Answers) characterizes a recent trend in web technologies, often referred to as the social web. This trend highlights the importance of supporting linguistic interactions in people\u27s online information-seeking activities in daily life - something that the web search engines still lack because of the complexity of this hu- man behavior. The presented research consists of an investigation of the information-seeking behavior of digital reference services through analysis of discourse semantics, called dialogue acts, and experimentation of automatic identification of dialogue acts using machine-learning techniques. The data was an online chat reference transaction archive, provided by the Online Computing Library Center (OCLC). Findings of the discourse analysis include supporting evidence of some of the existing theories of the information-seeking behavior. They also suggest a new way of analyzing the progress of information-seeking interactions using dia- logue act analysis. The machine learning experimentation produced promising results and demonstrated the possibility of practical applications of the DA analysis for further research across disciplines