Title :
Discriminative training of the pronunciation networks
Author :
Korkmazskiy, F. ; Juang, B.H.
Author_Institution :
Dialog Syst. Res. Dept., AT&T Bell Labs., Murray Hill, NJ, USA
Abstract :
Presents a new approach for a pronunciation network construction. The structure of a pronunciation network is determined as a result of the acoustical data decoding procedure that evaluates a list of the N most probable strings of pronunciation units (SPUs), such as phonemes. The importance of each of the decoded strings is characterized by a set of weight coefficients prescribed to phonemes or to some part of the phonemes. The optimality of the weight coefficients is defined in the framework of discriminative training, and the use of the minimum classification error (MCE) criterion allows us to maximize the discrimination between different pronunciation networks
Keywords :
decoding; hidden Markov models; learning (artificial intelligence); probability; speech recognition; acoustical data decoding procedure; decoded strings; discriminative training; minimum classification error criterion; most probable pronunciation unit strings; optimal weight coefficients; phonemes; pronunciation network construction; speech recognition; Acoustics; Decoding; Dictionaries; Hidden Markov models; Loudspeakers; Network topology; Radio access networks; Speech recognition; Statistics; Training data;
Conference_Titel :
Automatic Speech Recognition and Understanding, 1997. Proceedings., 1997 IEEE Workshop on
Conference_Location :
Santa Barbara, CA
Print_ISBN :
0-7803-3698-4
DOI :
10.1109/ASRU.1997.659009
