PHONETICALLY MOTIVATED ACOUSTIC PARAMETERS FOR CONTINUOUS SPEECH RECOGNITION USING ARTIFICIAL NEURAL NETWORKS
In the framework of an ANN/HMM hybrid system for phone recognition three specialized ANNs were designed and evaluated. One of these ANNs detects the manner of articulation. The other two ANNs describe the speech signal in terms of place of articulation. One of these is used for plosive and nasal classification, and the other one is used for fricative classification. The design of these networks was inspired by acoustic-phonetic knowledge. Input parameters, ANN topology and desired output representation have been optimized for the specific task of the network. Experiments are reported for the TIMIT database. Frame classification errors of 17.7% with the manner ANN (5 broad classes), 25.4% with the plosive and nasal ANN (10 phones). and 25.2% with the fricative ANN (11 phones) were obtained on a set of 616 sentences from 77 new speakers. Experiments for a prototype ANN/HMM hybrid system are also reported. We developed an algorithm for the global optimization of this hybrid system. The network for the manner of articulation and one network for the place of articulation were merged to a single ANN which outputs were modeled by an HMM. With this globally optimized hybrid system we achieved a recognition accuracy of 86% on an 8 class recognition problem (7 plosives and one class corresponding to all other phonemes).
acoustic parameters timit database global optimization anns (artificial neural networks) acoustic-phonetic decoding distinctive features ann hmm hybrid