Implementation of feature extraction and classification for speech dysfluencies - Lim, Sin Chee (School of Mechatronic Engineering, 2011)
Speech is prone to disruption of involuntary dysfluent events especially repetitions and prolongations of sounds, syllables and words which lead to dysfluency in communication. Traditionally, speech language pathologists count and classify occurrence of dysfluencies in flow of speech manually. However, these types of assessment are subjective, inconsistent, time-consuming and prone to error. In the last three decades, many research works have been developed to automate the conventional assessments with various approaches such as speech signal analysis, personal variables, acoustic analysis of speech signal and artificial intelligence techniques. From the previous works, it can be concluded that feature extraction methods and classification techniques play important roles in this research field. Therefore, in this work, there are few feature extraction methods, namely, Short Time Fourier Transform (STFT), Mel-frequency Cepstral Coefficient (MFCC) and Linear Predictive Coding (LPC) based parameterization were proposed to extract the salient feature of the two types of dysfluencies.