Hybrid HMM/ANN Systems for Speaker Independent Continuous Speech Recognition in French

Abstract

In this paper we report a series of tests carried out on our hybrid HMM/ANN systems which aims at combining Neural Networks theory and Hidden Markov Models (HMMs) for speech recognition of a continuous speech French database: BREF-80. As this database is not manually labelled, we describe a new method based on the temporal alignment of the speech signal on a high quality synthetic speech pattern to generate a first segmentation in order to bootstrap the training procedure. A phone recognition experiment with our baseline system achieved a phone accuracy of about 63% which is, to our knowledge, the best result reported in the litterature. Preliminary experiments on continuous speech recognition have set a baseline performance for our hybrid HMM/ANN system on BREF using 1K, 3K and 13 K word lexicons. All the experiments were carried out with the STRUT (Speech Training and Recognition Unified Toolkit) software [10]. I. Introduction Significant advances have been made in recent years in ..