Abstract
We propose a novel acoustic model for speech recognition, named FCD (Frame-based Context Dependent) model. It can obtain a probability distribution by using a top-down clustering technique to simultaneously consider the local frame position in phoneme, phoneme duration, and phoneme context. The model topology is derived from connecting left-to-right HMM models without self-loop transition for each phoneme duration. Because the FCD model can change the probability distribution into a sequence corresponding with one phoneme duration, it can has the ability to generate a smooth trajectory of speech feature vector. We also performed an experiment to evaluate the performance of speech recognition for the model. In the experiment, 132 questions for frame position, 66 questions for phoneme duration and 134 questions for phoneme context were used to train the sub-phoneme FCD model. In order to compare the performance, left-to-right HMM and two types of HSMM models with almost same number of states were also trained. As a result, 18% of relative improvement of tri-phone accuracy was achieved by the FCD model.
