Jiping Sun

Performance improvement of automatic speech recognition systems via multiple language models produced by sentence-based clustering

Grammar-based speech recognition systems exhibit performance degradation as their vocabulary sizes increase. Data clustering is deemed to reduce the proportionality of this problem. We introduce an approach to data clustering for automatic speech recognition systems using kohonen self-organized map. Clustering results are used further to build a language model for each of the clusters using CMU-Cambridge toolkit. The approach was implemented as a prototype for a large vocabulary and continuous speech recognition system and about 8% performance improvement was achieved in comparison with the performance achieved using the language model and dictionary provided by Sphinx3. We present the experimental results along with discussions, analysis and potential future directions.

Natural language understanding through fuzzy logic inference and its application to speech recognition

We report on a fuzzy logic-based language understanding system applied to speech recognition. This system acquires conceptual knowledge from corpus data and organizes such knowledge into fuzzy logic inference rules. The system parses speech recognition results into conceptual structures in a robust manner, and thus is able to tolerate noise caused by speech recognition errors. We discuss the fuzzy inference rule learning method and explain its organization. Experimental results that demonstrate the ability of the system to deal with complex speech input instances are reported.

A novel template matching approach to speaker-independent arabic spoken digit recognition

In this paper we propose a quantized time series algorithm for spoken word recognition. In particular, we apply the algorithm to the task of spoken Arabic digit recognition. The quantized time series algorithm falls into the category of template matching approach, but with two important extensions. The first is that instead of selecting some typical templates from a set of training data, all the data is processed through vector quantization. The second extension consists of a built-in temporal structure within the quantized time series to facilitate the direct matching, instead of relying on time warping techniques. Experimental results have shown that the proposed approach outperforms the time warping pattern matching schemes in terms of accuracy and processing time.

Fuzzy Methodology for Enhancement of Context Semantic Understanding

One of the many issues that confront traditional statistical approaches of natural language understanding (NLU) is on how to overcome the insufficient co-occurrence information caused by the limited boundary of statistical approaches. Researches have long used the imparting of human knowledge into statistical approaches, including definition of rules and collections of hierarchy of concepts. However, these are difficult to define even for a domain expert. They are also very much people and domain dependent. This study proposes a fuzzy approach to tackle these issues in a way as to provide a methodology for logical reorganizing context in order to tackle the issue of boundary limitation, to create the more reasonable and understandable word association which will be referenced as membership degree in latter stage, and to make the processes of imparting of human knowledge easier and less domain dependent. The accomplishment of these tasks could be achieved through the concept of precisiated natural language (PNL)

Comparison of GMM and fuzzy-GMM applied to phoneme classification

The increasing need for more natural human machine interfaces has generated intensive research work directed toward designing and implementing natural speech enabled systems. Because it is very hard to constrain a speaker when expressing a voice-based request, speech recognition systems have to be able to handle out of vocabulary words in the users speech utterance. In this paper, we investigate an approach that can be deployed in keyword spotting systems. We propose a phoneme classifier that will be ultimately used to provide confidence values to be compared against existing Automatic Speech Recognizer word confidences. The end goal is to build a keyword spotting system for natural language speech. The presented approach is based on fuzzy gaussian mixture modeling to carry out the English phonemes classification.