Reduced complexity tone classifier for automatic tonal speech recognizer

A tone classifier is an essential part of an automatic tonal speech recognizer (ATSR) because tonal languages recognize word meaning by tones. However, many researchers have developed a highly efficient tone recognition by using rich mathematical techniques and used the whole input speech as an input of pitch detection process. This paper proposes a reduced complexity tone classifier for the automatic tonal speech recognizer. The classifier reduces the number of input frames by detecting only the vowel signals as an input of the pitch detection, called vowel-AMDF (V-AMDF). The classifier uses a lower number of floating-point operations (FLOPs) than used in the whole input speech method. Due to the reduced number of FLOPs, this tone classifier can be suitable for portable electronic equipment. In addition, V-AMDF reduces F₀ contour errors caused by the influence from neighboring syllables. This proposed classifier was tested and set by 19 Thai words, selected from voice activation for GPS system and phone dialing options. The experimental results show 86.0% recognition accuracy, and 21.8% reduction in the number of FLOPs, compared with using the whole input speech.