Phoneme aware speech recognition through evolutionary optimisation

Phoneme awareness provides the path to high resolution speech recognition to overcome the difficulties of classical word recognition. Here we present the results of a preliminary study on Artificial Neural Network (ANN) and Hidden Markov Model (HMM) methods of classification for Human Speech Recognition through Diphthong Vowel sounds in the English Phonetic Alphabet, with a specific focus on evolutionary optimisation of bio-inspired classification methods. A set of audio clips are recorded by subjects from the United Kingdom and Mexico. For each recording, the data were pre-processed, using Mel-Frequency Cepstral Coefficients (MFCC) at a sliding window of 200ms per data object, as well as a further MFCC timeseries format for forecast-based models, to produce the dataset. We found that an evolutionary optimised deep neural network achieves 90.77% phoneme classification accuracy as opposed to the best HMM of 150 hidden units achieving 86.23% accuracy. Many of the evolutionary solutions take substantially longer to train than the HMM, however one solution scoring 87.5% (+1.27%) requires fewer resources than the HMM.