Developmental pattern analysis and age prediction by extracting speech features and applying various classification techniques

In speech development research, it's important to know how speech acoustic features vary as a function of age and the age when the variability and magnitude of acoustic features start to exhibit adult-like patterns. During the first few years of life, a child's speech changes from the cries and babbles of an infant to adult-like words and phrases of a young child. A number of acoustic studies observed that, adult's speech compared to children's speech, exhibits lower pitch and formant frequencies, shorter segmental durations, and lesser temporal and spectral variability. In this research we extracted acoustic, spectral and temporal features of a speech signal and then classify these features to predict the age of the subjects using different classification techniques. The feature vector comprised of fundamental frequency, formants, lpc coefficients and segmental duration. This study investigated the developmental patterns and the varying trends observed in speech acoustics with advancement in age and gender. The investigation then contributed in predicting the age of the speakers by analyzing these extracted features using various classification techniques and the result revealed maximum recognition rate by using neuro-fuzzy classifiers. This prediction of age may further help us in analyzing the speech samples in order to predict early speech disorders or language delay in children with neuro-developmental disorder.