Unsupervised Spoken Term Detection with Spoken Queries by Multi-level Acoustic Patterns with Varying Model Granularity

This paper presents a new approach for unsupervised Spoken Term Detection with spoken queries using multiple sets of acoustic patterns automatically discovered from the target corpus. The different pattern HMM configurations(number of states per model, number of distinct models, number of Gaussians per state)form a three-dimensional model granularity space. Different sets of acoustic patterns automatically discovered on different points properly distributed over this three-dimensional space are complementary to one another, thus can jointly capture the characteristics of the spoken terms. By representing the spoken content and spoken query as sequences of acoustic patterns, a series of approaches for matching the pattern index sequences while considering the signal variations are developed. In this way, not only the on-line computation load can be reduced, but the signal distributions caused by different speakers and acoustic conditions can be reasonably taken care of. The results indicate that this approach significantly outperformed the unsupervised feature-based DTW baseline by 16.16\% in mean average precision on the TIMIT corpus.Comment: Accepted by ICASSP 201

Design research on systems thinking approach in veterinary education

The purpose of this study was to investigate the application of a newly designed systems approach to the problem of students\u27 lack of big-picture experience in the College of Veterinary Medicine. To determine whether students\u27 performance on problem-solving for various scenarios improved after intervention , a design research methodology was adopted to develop a systems-approach teaching and learning environment. Three iterations were conducted, with improvements to the instructional approach following each of the first two iterations. The results supported the hypothesis that instructional intervention led to modest but statistically significant increases in students\u27 use of system thinking across the three experimental studies. However, the instructor indicated the need for faculty systems-approach training, whereas students tended to request hands-on practice to understand and retain systems thinking skills. Furthermore, there was a significant improvement from pretest to posttest for the beef scenario, demonstrating transfer of systems thinking to a topic for which systems-approach instruction was not provided. The qualitative data suggested that most students found systems thinking was beneficial for macro systems, such as food production, but not for micro systems such, as individual small-animal biological systems