TY - CHAP A1 - Riedhammer, Korbinian A1 - Gropp, Martin A1 - Nöth, Elmar T1 - The FAU Video Lecture Browser System T2 - 2012 IEEE Spoken Language Technology Workshop (SLT), Miami, FL, USA, December 2012. N2 - A growing number of universities and other educational institutions provide recordings of lectures and seminars as an additional resource to the students. In contrast to educational films that are scripted, directed and often shot by film professionals, these plain recordings are typically not post-processed in an editorial sense. Thus, the videos often contain longer periods of inactivity or silence, unnecessary repetitions, or corrections of prior mistakes. This paper describes the FAU Video Lecture Browser system, a web-based platform for the interactive assessment of video lectures, that helps to close the gap between a plain recording and a useful e-learning resource by displaying automatically extracted and ranked key phrases on an augmented time line based on stream graphs. In a pilot study, users of the interface were able to complete a topic localization task about 29 % faster than users provided with the video only while achieving about the same accuracy. The user interactions can be logged on the server to collect data to evaluate the quality of the phrases and rankings, and to train systems that produce customized phrase rankings. KW - automatic speech recognition KW - key phrase extraction KW - key phrase ranking KW - visualization KW - user interaction Y1 - 2012 SP - 392 EP - 397 PB - IEEE ER - TY - CHAP A1 - Riedhammer, Korbinian A1 - Bocklet, Tobias A1 - Goshal, Arnab A1 - Povey, Daniel T1 - Revisiting Semi-Continuous Hidden Markov Models T2 - 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan, March 2012. N2 - In the past decade, semi-continuous hidden Markov models (SCHMMs) have not attracted much attention in the speech recognition community. Growing amounts of training data and increasing sophistication of model estimation led to the impression that continuous HMMs are the best choice of acoustic model. However, recent work on recognition of under-resourced languages faces the same old problem of estimating a large number of parameters from limited amounts of transcribed speech. This has led to a renewed interest in methods of reducing the number of parameters while maintaining or extending the modeling capabilities of continuous models. In this work, we compare classic and multiple-codebook semi-continuous models using diagonal and full covariance matrices with continuous HMMs and subspace Gaussian mixture models. Experiments on the RM and WSJ corpora show that while a classical semicontinuous system does not perform as well as a continuous one, multiple-codebook semi-continuous systems can perform better, particular when using full-covariance Gaussians. KW - automatic speech recognition KW - acoustic modeling Y1 - 2012 SP - 4721 EP - 4724 PB - IEEE ER -