Abstract not found

This report uses a bark scale, which has been replaced here with a mel-scale. CHAPTER 3. THE ABBOT SPEECH RECOGNITION SYSTEM 32 where, ¯ i = 1

ABBOT is the hybrid connectionist-hidden Markov model largevocabulary speech recognition system developed at Cambridge University. In this system, a recurrent network maps each acoustic vector to an estimate of the posterior probabilities of the phone classes, which are used as observation probabilities within an HMM. This paper describes the system which participated in the November 1995 ARPA Hub-3 Multiple Unknown Microphones (MUM) evaluation of continuous speech recognition systems, under the guise of the CU-CON system. The emphasis of the paper is on the changes made to the 1994 ABBOT system, specifically to accomodate the H3 task. This includes improved acoustic modelling using limited word-internal context-dependentmodels, training on the Wall Street Journal secondary channel database, and using the linear input network for speaker and environmental adaptation. Experimental results are reported for various test and development sets from the November 1994 and 1995 ARPA benchmark tests.

ABBOT is the hybrid connectionist hidden Markov model (HMM) large vocabulary continuous speech recognition system developed at Cambridge University Engineering Department. abbot makes effective use of the linear input network (LIN) adaptation technique to achieve speaker and channel adaptation. Although the LIN is effective at adapting to new speakers or a new environment (e.g. a different microphone), the transform is global over the input space. In this paper we describe a technique by which the transform may be made locally linear over different regions of the input space. The local linear transforms are combined by an additional network using a non-linear transform. This scheme falls naturally into the mixtures of experts framework.