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

Abstract

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 neurodevelopmental disorder.

Fuzzy Utilization in Speech Recognition and its Different Application

“…Furthermore, the investigations verifed the masterminded strategy has some higher results than those maltreatment the techniques. Gautam, et al (2015). Irrefutably, disfluency of talk among youngsters is dissected maintained the understandability assessment by talk and language pathologists, which may be past a sensible uncertainty won and long.…”

Predictive biometrics: a review and analysis of predicting personal characteristics from biometric data

“…The investigation of age prediction from gait data can be found in [44,105], while [46,106] investigate the prediction of age from iris data using different sets of geometric and texture features. In [45,100,107], the authors have investigated the prediction of age from voice data, and Merkel et al [108] examines the prediction of subject age from the fingerprint. Predictive accuracies range from a minimum of 57% (for the iris modality) to a maximum of 77% (for the signature modality), but the problems noted above make drawing specific conclusions unwise.…”

A new approach with score-level fusion for the classification of a speaker age and gender

“…However, low-arousal speeches are classified by 98.9% of success rate according to their genders, and 61.3% success rate according their ages. In another study, 134 children whose ages varying between 4 and 8, and 18 adults are classified according to their age groups [8] . Adult speakers are regarded as one group, and children are classified into 5 different groups according to their ages.…”