Complexity and metricality

Abstract

In this work, consonant aspiration and unaspiration phenomena are studied. It is known that, pronunciation of aspiration and unaspiration is characterized by the ’puff of air’ released at the place of constriction in the vocal tract also known as burst. Here, properties of the vowel immediately after the burst are studied for characterization of the burst. Excitation source signal estimated from speech as low pass filtered linear prediction residual signal is used for the task. The signal characteristics of parameters such as glottal pulse, duration of open, closed & return phases; slope of open, & return phases; duration of burst; ratio of highest and lowest frame wise energies of signal and voice onset point are explored as features to characterize aspiration and unaspiration. Three datasets namely TIMIT, IIIT Hyderabad Marathi and IIIT Hyderabad Hindi (IIIT-H Indic Speech Databases) are used to verify the proposed approach. Random forest, support vector machine and deep feed forward neural networks (DFFNNs) are used as classifiers to test the effectiveness of the features used for the task. Optimal features are selected for the classification using correlation based feature selection (CFS). From the results, it is observed that the proposed features are efficient in classifying the aspirated and unaspirated consonants. Performance of the proposed features in recognition of aspirated and unaspirated phoneme is also evaluated. IIIT Hyderabad Marathi is considered for the analysis. It is observed that the performance of recognition of aspirated and unaspirated sounds using proposed features is improved in comparison with the MFCCs based phoneme recognition system.