Accession Number : ADA290655
Title : High-Performance Speech Recognition Using Consistency Modeling.
Descriptive Note : Final rept.,
Corporate Author : SRI INTERNATIONAL MENLO PARK CA
Personal Author(s) : Digalakis, Vassilios ; Murveit, Hy ; Monaco, Peter ; Neumeyer, Leo ; Sankar, Ananth
PDF Url : ADA290655
Report Date : DEC 1994
Pagination or Media Count : 149
Abstract : The goal of SRI's consistency modeling project is to improve the raw acoustic modeling component of SRI's DECIPHER speech recognition system and develop consistency modeling technology. Consistency modeling aims to reduce the number of improper independence assumptions used in traditional speech recognition algorithms so that the resulting speech recognition hypotheses are more self-consistent and, therefore, more accurate. At the initial stages of this effort, SRI focused on developing the appropriate base technologies for consistency modeling. We first developed the Progressive Search technology that allowed us to perform large-vocabulary continuous speech recognition (LVCSR) experiments. Since its conception and development at SRI, this technique has been adopted by most laboratories, including other ARPA contracting sites, doing research on LVSR. Another goal of the consistency modeling project is to attack difficult modeling problems, when there is a mismatch between the training and testing phases. Such mismatches may include outlier speakers, different microphones and additive noise. We were able to either develop new, or transfer and evaluate existing, technologies that adapted our baseline genonic HMM recognizer to such difficult conditions. (AN)
Descriptors : *SPEECH RECOGNITION, *PATTERN RECOGNITION, ALGORITHMS, EXPERIMENTAL DATA, MODELS, REAL TIME, PERFORMANCE(ENGINEERING), CONSISTENCY, ADAPTIVE SYSTEMS, ACOUSTIC SIGNALS, KNOWLEDGE BASED SYSTEMS, ACOUSTIC FILTERS, VOCABULARY, MICROPHONES, MARKOV PROCESSES, BACKGROUND NOISE, ERROR CORRECTION CODES, NATURAL LANGUAGE, WORD RECOGNITION.
Subject Categories : Cybernetics
Distribution Statement : APPROVED FOR PUBLIC RELEASE