Tata Consultancy Services Limited

Venkata Subramanian Viraraghavan

Ramesh Kumar Ramakrishnan

Rahul Dasharath Gavas

Sachin Patel

Mithun Basaralu Sheshachala

Gauri Deshpande

Arpan Pal

Balamuralidhar Purushothaman

Debatri Chatterjee

Achanna Anil Kumar

Dibyanshu Jaiswal

Kartik Muralidharan

Girish Mariswamy Chandra

Mayuri Duggirala

Tanushree Bannerjee

Gauri Ashutosh Deshpande

An important task in several wellness applications is detection of emotional valence from speech. Two types of features of speech signals are used to detect valence: acoustic features and text features. Acoustic features are derived from short frames of speech, while text features are derived from the text transcription. Present disclosure provides systems and methods that determine the effect of text on acoustic features. Acoustic features of speech segments carrying emotion words are to be treated differently from other segments that do not carry such words. Only specific speech segments of the input speech signal are considered based on a dictionary specific to a language to assess emotional valence. A model trained (or trained classifier) for specific language either by including the acoustic features of the emotion related words or by omitting it is used by the system for determining emotional valence in an input speech signal.

Selection of speech segments for training classifiers for detecting emotional valence from input speech signals

This disclosure relates generally to a method and system for assessment of sustained visual attention of a target. The conventional methods utilize various markers for assessment of attention, however, blink rate variability (BRV) series signal is not explored yet. In an embodiment, the disclosed method utilizes BRV series signal for assessing sustained visual attention of a target. A gaze data of the target is recorded using an eye tracker and a set of uniformly sampled BRV series signal is reconstructed from each of the BRV series. One or more frequency domain features, including pareto frequency, are extracted from the set of uniformly sampled BRV series signal. The values of frequency domain features extracted from the set of BRV series signals are compared with corresponding threshold values to determine visual sustained attention of the target.

System and method for eye-blink based assessment of sustained visual attention of a target

Embodiments herein provide a method and system for continuously validating a user during an established authenticated session using Photoplethysmogram (PPG) and accelerometer data. State of the art approaches are mostly based on feature extraction and ML modelling for PPG based continuous session validation, while a template based approach in the art follows a complicated approach. The method disclosed herein utilizes less computation intensive template based approach to continuously validate the user across the session. The method comprises preprocessing a PPG data or PPG signal acquired from a wearable device worn by the user to identify segments of negligible motion. A first segment, after authentication using conventional authentication mechanism, serves as the initial reference. The chosen segments are then tested one by one with respect to the reference. If the templates in a segment match those of the reference, it is updated as the new reference, else a re-authentication is triggered.

Continuously validating a user during an established authenticated session using photoplethysmogram and accelerometer data

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

The present subject matter discloses a method implemented on a base station for generating Physical Random Access Channel (PRACH) reference segments and detecting PRACH preambles in a Long Term Evolution (LTE) communication system. The base station performs a series of mathematical techniques to generate the PRACH reference segments using a CAZAC sequence used for detecting PRACH preambles. Further, the base station identifies the PRACH by using a signal received by the base station. The base station segments the signal to generate contiguous or non-contiguous segments. Further, the base station uses a segment by segment multiplication and subsequent addition approach performed between values at each frequency location of PRACH frequency segments with a complex conjugate of the values at a corresponding frequency location of a PRACH reference segment. Subsequently the products are added together. The sum is then processed to detect one or more PRACH preambles and timing delays.

Detecting physical random access channel preambles in a long term evolution communication system

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Bengaluru, India

Venkata Subramanian Viraraghavan