HMM Topology in Continuous Speech Recognition Systems

摘要

Nowadays, HMM-based speech recognition systems are used in many real time processing applications, from cell phones to automobile automation. In this context, one important aspect to be considered is the HMM model size, which directly determines the computational load. So, in order to make the system practical, it is interesting to optimize the HMM model size constrained to a minimum acceptable recognition performance. Furthermore, topology optimization is also important for reliable parameter estimation. This work presents the new Gaussian Elimination Algorithm (GEA) for determining the more suitable HMM complexity in continuous speech recognition systems. The proposed method is evaluated on a small vocabulary continuous speech (SVCS) database as well as on the TIMIT corpus.