Paper
A comparative study on hybrid acoustic phonetic decoders based on artificial neural networks
In this paper we compare two hybrid acoustic-phonetic decoders based on Artificial Neural Networks (ANN). We evaluate them on the task of recognizing stop phones in continuous speech independently from the speaker. ANNs are well suited to perform detailed phonetic distinctions. In general, techniques based on Dynamic Programming (DP), in particular Hidden Markov Models (HMMs), have proven to be successful at modeling the temporal structure of the speech signal. In the approach described here, the ANN outputs constitute the sequence of observation vectors for the HMM. An algorithm is proposed for global optimization of all the parameters of the ANN/HMM decoder. Comparative experiments using this ANN/HMM hybrid decoder and another ANN-DP hybrid are reported for the TIMIT database. 1 Introduction Artificial Neural Networks (ANNs) effectively perform phonetic classification, but have not proven yet to model the temporal structure of the speech signal reasonably well [Lip89, Rob90, Ben90b...
Authors: Yoshua Bengio · Renato De Mori · Giovanni Flammia · Ralf Kompe