Vinay Kumar Mittal

Also published as: Vinay Kumar Mittal


2019

pdf bib
Non-native Accent Partitioning for Speakers of Indian Regional Languages
Radha Krishna Guntur | Krishnan Ramakrishnan | Vinay Kumar Mittal
Proceedings of the 16th International Conference on Natural Language Processing

Acoustic features extracted from the speech signal can help in identifying speaker related multiple information such as geographical origin, regional accent and nativity. In this paper, classification of native speakers of South Indian languages is carried out based upon the accent of their non-native language, i.e., English. Four South Indian languages: Kannada, Malayalam, Tamil, and Telugu are examined. A database of English speech from the native speakers of these languages, along with the native language speech data was collected, from a non-overlapping set of speakers. Segment level acoustic features F0 and Mel-frequency cepstral coefficients (MFCCs) are used. Accent partitioning of non-native English speech data is carried out using multiple classifiers: k-nearest neighbour (KNN), linear discriminant analysis (LDA) and support vector machine (SVM), for validation and comparison of results. Classification accuracies of 86.6% are observed using KNN, and 89.2% or more than 90% using SVM classifier. A study of acoustic feature F0 contour, related to L2 intonation, showed that native speakers of Kannada language are quite distinct as compared to those of Tamil or Telugu languages. It is also observed that identification of Malayalam and Kannada speakers from their English speech accent is relatively easier than Telugu or Tamil speakers.

pdf bib
Autism Speech Analysis using Acoustic Features
Abhijit Mohanta | Vinay Kumar Mittal
Proceedings of the 16th International Conference on Natural Language Processing

Autism speech has distinct acoustic patterns, different from normal speech. Analyzing acoustic features derived from the speech of children affected with autism spectrum disorder (ASD) can help its early detection. In this study, a comparative analysis of the discriminating acoustic characteristics is carried out between ASD affected and normal children speech, from speech production point of view. Datasets of English speech of children affected with ASD and normal children were recorded. Changes in the speech production characteristics are examined using the excitation source features F0 and strength of excitation (SoE), the vocal tract filter features formants (F1 to F5) and dominant frequencies (FD1, FD2), and the combined source-filter features signal energy and zero-crossing rate. Changes in the acoustic features are compared in the five vowels’ regions of the English language. Significant changes in few acoustic features are observed for ASD affected speech as compared to normal speech. The differences between the mean values of the formants and dominant frequencies, for ASD affected and normal children, are highest for vowel /i/. It indicates that ASD affected children have possibly more difficulty in speaking the words with vowel /i/. This study can be helpful towards developing systems for automatic detection of ASD.

2018

pdf bib
Infant Crying Cause Recognition using Conventional and Deep Learning based Approaches
Shivam Sharma | P. Viswanath | Vinay Kumar Mittal
Proceedings of the 15th International Conference on Natural Language Processing

pdf bib
Analyzing Autism Speech of Children in English Vowels Regions by Analysis of Changes in Production Features
Abhijit Mohanta | Vinay Kumar Mittal
Proceedings of the 15th International Conference on Natural Language Processing