Fuzzy GMM-based Confidence Measure Towards Keywords Spotting Application

The increasing need for more natural human machine interfaces has generated intensive research work directed toward designing and implementing natural speech enabled systems. The Spectrum of speech recognition applications ranges from understanding simple commands to getting all the information in the speech signal such as words, meaning and emotional state of the user. Because it is very hard to constrain a speaker when expressing a voice-based request, speech recognition systems have to be able to handle (by filtering out) out of vocabulary words in the users speech utterance, and only extract the necessary information (keywords) related to the application to deal correctly with the user query. In this thesis, we investigate an approach that can be deployed in keyword spotting systems. We propose a confidence measure feedback module that provides confidence values to be compared against existing Automatic Speech Recognizer word confidences. The feedback module mainly consists of a soft computing tool-based system using fuzzy Gaussian mixture models to identify all English phonemes. Testing has been carried out on the JULIUS system and the preliminary results show that our feedback module outperforms JULIUS confidence measures for both the correct spotted words and the falsely mapped ones. The results obtained could be refined even further using other type of confidence measure and the whole system could be used for a Natural Language Understanding based module for speech understanding applications

Personalized Fuzzy Text Search Using Interest Prediction and Word Vectorization

In this paper we study the personalized text search problem. The keyword based search method in conventional algorithms has a low efficiency in understanding users' intention since the semantic meaning, user profile, user interests are not always considered. Firstly, we propose a novel text search algorithm using a inverse filtering mechanism that is very efficient for label based item search. Secondly, we adopt the Bayesian network to implement the user interest prediction for an improved personalized search. According to user input, it searches the related items using keyword information, predicted user interest. Thirdly, the word vectorization is used to discover potential targets according to the semantic meaning. Experimental results show that the proposed search engine has an improved efficiency and accuracy and it can operate on embedded devices with very limited computational resources