A mobile fitness companion

The paper introduces a Mobile Companion prototype, which helps users to plan and keep track of their exercise activities via an interface based mainly on speech input and output. The Mobile Companion runs on a PDA and is based on a stand-alone, speaker-independent solution, making it fairly unique among mobile spoken dialogue systems, where the common solution is to run the ASR on a separate server or to restrict the speech input to some specific set of users. The prototype uses a GPS receiver to collect position, distance and speed data while the user is exercising, and allows the data to be compared to previous exercises. It communicates over the mobile network with a stationary system, placed in the user’s home. This allows plans for exercise activities to be downloaded from the stationary to the mobile system, and exercise result data to be uploaded once an exercise has been completed

A pollen identification expert system ; an application of expert system techniques to biological identification : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Computer Science Massey University

The application of expert systems techniques to biological identification has been investigated and a system developed which assists a user to identify and count air-borne pollen grains. The present system uses a modified taxonomic data matrix as the structure for the knowledge base. This allows domain experts to easily assess and modify the knowledge using a familiar data structure. The data structure can be easily converted to rules or a simple frame-based structure if required for other applications. A method of ranking the importance of characters for identifying each taxon has been developed which assists the system to quickly narrow an identification by rejecting or accepting candidate taxa. This method is very similar to that used by domain experts

Factor analysis modelling for speaker verification with short utterances

This paper examines combining both relevance MAP and subspace speaker adaptation processes to train GMM speaker models for use in speaker verification systems with a particular focus on short utterance lengths. The subspace speaker adaptation method involves developing a speaker GMM mean supervector as the sum of a speaker-independent prior distribution and a speaker dependent offset constrained to lie within a low-rank subspace, and has been shown to provide improvements in accuracy over ordinary relevance MAP when the amount of training data is limited. It is shown through testing on NIST SRE data that combining the two processes provides speaker models which lead to modest improvements in verification accuracy for limited data situations, in addition to improving the performance of the speaker verification system when a larger amount of available training data is available