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Hypernasality refers to the perception of excessive nasal resonances in vowels and voiced consonants. Existing speech processing based approaches concentrate only on the classification of speech into normal or hypernasal, which do not give the degree of hypernasality in terms of continuous values like nasometer. Motivated by the functionality of nasometer, in this work, a method is proposed for the evaluation of hypernasality. Speech signals representing two extremely opposite cases of nasality are used to develop the acoustic models, where oral sentences (rich in vowels, stops, and fricatives) of normal speakers and nasal sentences (rich in nasals and nasalized vowels) of moderate-severe hypernasal speakers represent the groups with minimum and maximum attainable degrees of nasality, respectively. The acoustic features derived from glottal activity regions are used to model the maximum and minimum nasality classes using Gaussian mixture model and deep neural network approaches. The posterior probabilities obtained for nasal sentence class are referred to as hypernasality scores. The scores show a significant correlation (p < 0.01) with respect to perceptual ratings of hypernasality, provided by expert speechlanguage pathologists. Further, hypernasality scores are used for the detection of hypernasality, and the results are compared with the nasometer based approach.

A method to detect spoken keywords in a given speech utterance is proposed, called as joint Dynamic Time Warping (DTW)-Convolution Neural Network (CNN). It is a combination of DTW approach with a strong classifier like CNN. Both these methods have independently shown significant results in solving problems related to optimal sequence alignment and object recognition, respectively. The proposed method modifies the original DTW formulation and converts the warping matrix into a gray scale image. A CNN is trained on these images to classify the presence or absence of keyword by identifying the texture of warping matrix. The TIMIT corpus has been used for conducting experiments and our method shows significant improvement over other existing techniques.

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