| Phoneme expansion is made from mono-phone to tri-phone because of context, and the number of acoustic models increases sharply. Each state of every model consists of multiple Gaussian mixtures for high accuracy and a large-scale Hidden Markov Model contains quite a large number of Gaussian mixtures. As a consequence, storing these Gaussian mixtures requires substantial amounts of memory and calculating likelihood probability of all states takes a lot of time. That results in high real time factor for most auto speech recognition systems. We study technologies about acoustic model compressing to overcome these two problems.This paper focus on two technologies:Gaussian mixture tying and Gaussian selection.1.Gaussian mixture tying:traditional Gaussian mixture tying and subspace distribution clustering.We research about traditional Gaussian mixture tying and subspace distribution clustering. This paper makes a research about deciding whether clustering algorithms and distance measure criterion have an important influence on traditional Gaussian mixture tying. Besides, weinvestigate subspace and the number of code words of each subspace which are two key techniques belonging to subspace distribution clustering.2.A new Monophone State-Based Gaussian selection.We make a research about standard Gaussian selection, traditional Gaussian selection and find that these technologies all have some disadvantages. A new Gaussian selection technology is presented to weaken influence made by these disadvantages. The new Gaussian selection technology is proved to be effective after a lot of tests.3. A new Tied-Mixture-Based Gaussian selection.The aim of Gaussian selection technology is to reduce amount of computation during decoding process, so substantial amounts of memory is still required. A widespread use of Gaussian selection technology is impossible owing to this disadvantage. This paper proposes a new technology based on Gaussian mixture tying and Gaussian selection for lowering memory usage and reducing decoding time. |
