Performance Comparison of Phoneme Modeling using PNCC and RASTA PLP Features with various Optimization Algorithms for Neural Network Architectures
Speech is the basic means of human communication. It contains a lot of information in addition to the content one is conveying and words being spoken. These days, humans try to effectively communicate with machines. So, understanding of speech has become potentially useful. To communicate with machines, conversion of speech into text is required. This is the task of Automatic Speech Recognition (ASR) system, which is a subfield of Artificial Intelligence (AI) Phonemes are distinct speech sounds that a language uses. This work focuses on phoneme modeling in English language using Artificial Neural Networks (ANN) Experiments are carried out using PNCC (Power Normalized Cepstral Coefficients) and RASTA PLP (Relative Spectral Transform Perceptual Linear Prediction) features and the results for different input sizes (corresponding to the number of adjacent frames of feature vectors used, containing different amount of contexual information) optimizing training functions, number of hidden layers and number of hidden nodes for the ANN are compared.
2019 9th International Conference on Advances in Computing and Communication (ICACC)