Using LVQ and SOM to train mixture density HMMs for phonemes

Mikko Kurimo

The objective of this research topic is to study and test how neural network algorithms can be applied to phoneme recognition with hidden Markov models.

HMMs are widely used in automatic speech recognition as phoneme models to combine the modeling of stationary stochastic processes producing observable short-time features and the temporal relationships between these processes. The temporal model in HMMs relies on a relatively simple structure of successive states and a probabilistic model of their mutual transitions. In mixture density HMMs the modeling of the stochastic observation processes associated with the states of HMMs is based on the estimation of the probability density function of the short-time observations in each state as a mixture of Gaussian densities.

The main research effort is concentrated in the training of the Gaussians codebooks to represent the different phoneme states as well as possible and to be able to optimally discriminate between different phonemes. The neural network methods developed in our laboratory, especially Learning Vector Quantization and Self-Organizing Maps, have special properties that suit very well to this training task. The SOMs are used in the initialization phase, where a smooth mapping of the set of available training vectors has provided a productive basis for the actual estimation. The obtained codebook topology can also be used in later phases, e. g., to speed up the calculations to approximate the observation probabilities. LVQ was applied to increase discrimination between different phoneme models in the initialization phase, in the actual training as a modification of the Viterbi estimation or as an error corrective tuning method to eliminate observed recognition errors.

In the experiments LVQ and SOMs are applied in association with the more traditional iterative maximum likelihood methods to conform a hybrid training scheme that provides reductions in both the phoneme recognition error rate and speed compared to the maximum likelihood methods alone.

Julkaisuluettelo

[1] Mikko Kurimo. Application of Learning Vector Quantization and Self-Organizing Maps for training continuous density and semi-continuous Markov models. Licentiate’s thesis, Helsinki University of Technology, Espoo, Finland, 1994.

[2] Mikko Kurimo. Corrective tuning by applying LVQ for continuous density and semicontinuous Markov models. In Proceedings of International Symposium on Speech, Image Processing and Neural Networks, volume 2, pages 718–721, Hong Kong, April 1994.

[3] Mikko Kurimo. Hybrid training method for tied mixture density hidden Markov models using Learning Vector Quantization and Viterbi estimation. In Proceedings of the IEEE Workshop on Neural Networks for Signal Processing, pages 362–371, Ermioni, Greece, September 1994.