New speech recognition approaches based upon finite state vector quantization with structural constraints

The label-transition finite-state vector-quantization (FSVQ) algorithm is extensively explored to exhibit the power of finite-state machines for speech recognition. It is found that the FSVQ algorithm combined with special structural constraints can discriminate a finite set of candidates very successfully. All the consonant initials of isolated Mandarin monosyllables from designated speakers are used as the example vocabulary in the simulation. In addition to utilizing the first order memory provided by FSVQ on speech recognition, an experiment is conducted that expands the FSVQ to use the second-order memory and the dynamic relationship among the components of this three-vector group are used for recognition. The simulation results show that a slightly higher recognition rate (94.4%) is obtained with a consistent prediction interval