Tata Consultancy Services Limited

Sunilkumar Kopparapu

Ashish Panda

Arun Pande

Bhavikkumar Bhagvanbhai Vachhani

Sonal Joshi

Chitralekha Ganapati Bhat

With recent real-world applications of speaker and speech recognition systems, robust features for degraded speech have become a necessity. In general, degraded speech results in poor performance of any speech-based system. This poor performance can be attributed to feature extraction functionality of speech-based system which takes input speech file and converts it into a representation called as a feature. Embodiments of the present disclosure provide systems and methods that compute distance between each degraded speech feature extracted from an input speech signal with each clean speech feature comprised in a memory of the system to obtain set of matched clean speech features wherein at least a subset of cleaned speech features are dynamically selected based on a pre-defined threshold and the computed distance, thereby computing statistics for the dynamically selected clean speech features set for utilizing in at least one of a speech recognition system and a speaker recognition system.

Features search and selection techniques for speaker and speech recognition

This disclosure relates generally to speech signal processing, and more particularly to method and system for improving recognition of disordered speech. In one embodiment, a method and a system for identifying extent of deviation in speech utterances of a user from a normal level, caused due to such impairments are provided. The system further makes appropriate modifications to generate utterances pertaining to healthy speech. This data may be fed as input to the speech recognition systems, as those systems can interpret the corrected data.

Method and system for improving recognition of disordered speech

A self configuring knowledge representation system and method is presented which self configures based on the external stimuli in order to answer the query in a way best suited to the user. The domain knowledge representation here is by way of graphs that allows the knowledge to self configure based on the query intent to give 'specific' to the query answers.

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Thane, India

Sunilkumar Kopparapu