Search

This paper describes ANN based posterior estimates and their application to speech recognition. We replaced the standard back-propagation with the L-BFGS quasi-Newton method. We have focused only on posterior based feature vector extraction. Our goal was a feature vector dimension reduction. Thus we designed three posterior transforms to space with dimensionality 1 or 2. The designed transforms were tested on the SpeechDat-East corpus. We also applied the introduced method on a Czech audio-visual corpus. In both cases the methods leads to significant word error rate decrease.

In this paper we describe the system for a fast phonetic/lexical searching in the large archives of the Czech holocaust testimonies. The developed system is the first step to a fulfillment of the MALACH project visions [1, 2], at least as for an easier and faster access to the Czech part of the archives. More than one thousand hours of spontaneous, accented and highly emotional speech of Czech holocaust survivors stored at the USC Shoah Foundation Institute as videointerviews were automatically transcribed and phonetically/lexically indexed. Special attention was paid to processing of colloquial words that appear very frequently in the Czech spontaneous speech. The final access to the archives is very fast allowing to detect segments of interviews containing pronounced words, clusters of words presented in pre-defined time intervals, and also words that were not included in the working vocabulary (OOV words).