Tata Consultancy Services Limited

Mayuri Duggirala

Sachin Patel

Deepa Tavargeri Adiga

Maitry Bhavsar

Venkata Subramanian Viraraghavan

Gauri Deshpande

Mukul Malik

Vaibhav Sharma

Vivek Balaraman

Gauri Ashutosh Deshpande

This disclosure relates generally to methods and systems for automatic extraction of self-reported activities of an individual from a freestyle narrative text. Manual extraction of such self-reported activities of the individual from the freestyle narrative text over the period of time is a complex task and consume a significant amount of time. The present systems and methods utilize a predefined grammar pattern and a natural language processing technique to generate one or more candidate activity phrases, from the pre-processed input text posted by the individual. A deep learning based supervised classification model is utilized to automatically extract the one or more self-reported activities of the individual, from the one or more candidate activity phrases. Manual intervention and efforts of analyzing the freestyle narrative text to extract the self-reported activities are avoided. Longitudinal assessment of the self-reported activities may reveal routines and behavior of the individual.

Methods and systems for automatic extraction of self-reported activities of an individual

Method and system to extract domain concepts to create domain dictionaries and ontologies comprises collecting a plurality of reference papers and further classifying the collected plurality of reference papers as relevant and irrelevant. Each of the 'relevant' reference papers is further processed by the system, during which the system identifies relevant sections from each document and further processes data in the relevant sections to extract required information and also to identify a relationship between different extracted information, which is further used to create domain dictionaries and ontologies.

Method and system to extract domain concepts to create domain dictionaries and ontologies

This disclosure relates generally to speech signal processing, and more particularly to a method and system for processing speech signal for emotion identification. The system processes a speech signal collected as input, during which a plurality of differential features corresponding to a plurality of frames of the speech signal are extracted. Further, the differential features are compared with an emotion recognition model to identify at least one emotion matching the speech signal, and then the at least one emotion is associated with the speech signal.

Method and system using successive differences of speech signals for emotion identification

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Pune, India

Mayuri Duggirala