Using MATLAB for Analysis of TRAP System

This article describes a Matlab function for reading and processing file outputs from a structure of classifiers. These classifiers - neural nets - are used in speech recognition based on temporal trajectories (TRAP) of energy in frequency bands. This nonstandard approach is introduced and the program is presented. The utility of resulting figures is enhanced by the possibility of reading and displaying results from all critical bands at once. Resulting analyses are more focused on the reliability of classifiers than on the basic accuracy measure. The first uses colors and their depth to display both cues, reliability and accuracy, in one informative picture. Others are focused on more precise measures, where it is possible to precisely define classifier mistakes

Distributed Speech Recognition

This article discusses possibilities of integrating speech technology into wireless technology, allowing voice input for wireless devices. Distributed speech recognition concept and activities related to its standardization are presented. First ETSI DSR MFCC based standard is described. Work on its extension to improve robustness resulting in new standard is also presented

Audio Surveillance through Known Event Classification

The way of audio surveillance through known event classification is presented introducing simple yet efficient framework. The use of the proposed system for unknown event detection is also suggested and evaluated. Further, a specific audio event is detected with use of audio classification, which helps the detection to focus on a signal of specific behavior. Thus it is shown that the system can be used in several applications

Transcribing meetings with the AMIDA systems

In this paper, we give an overview of the AMIDA systems for transcription of conference and lecture room meetings. The systems were developed for participation in the Rich Transcription evaluations conducted by the National Institute for Standards and Technology in the years 2007 and 2009 and can process close talking and far field microphone recordings. The paper first discusses fundamental properties of meeting data with special focus on the AMI/AMIDA corpora. This is followed by a description and analysis of improved processing and modeling, with focus on techniques specifically addressing meeting transcription issues such as multi-room recordings or domain variability. In 2007 and 2009, two different strategies of systems building were followed. While in 2007 we used our traditional style system design based on cross adaptation, the 2009 systems were constructed semi-automatically, supported by improved decoders and a new method for system representation. Overall these changes gave a 6%-13% relative reduction in word error rate compared to our 2007 results while at the same time requiring less training material and reducing the real-time factor by five times. The meeting transcription systems are available at www.webasr.org