Segmental LVQ3 training for phoneme-wise tied mixture density HMMS

Abstract

This work presents training methods and recognition experiments for phoneme-wise tied mixture densities in hidden Markov models (HMM). The system trains speaker dependent, but vocabulary independent, phoneme models for the recognition of Finnish words. The Learning Vector Quantization (LVQ) methods are applied to increase the discrimination between the phoneme models. A segmental LVQ3 training is proposed to substitute the LVQ2 based corrective tuning as a parameter estimation method. The experiments indicate that the new method can provide the corresponding recognition accuracy, but with less training and more robustness over the initial models. Experiments to upscale the current system by introducing context vectors and larger mixture pools show up to 40 % reduction of recognition errors compared to the earlier results in [10].