Hybrid Hmm/ann Systems for Speaker Independent Continuous Speech Recognition in French
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چکیده
| 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 rst 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 Uniied Toolkit) software 10].
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تاریخ انتشار 2007