Prosodic Featured Based Automatic Language Identification

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Living beings inherently have the ability to differentiate languages as a part of human intelligence. Language Identification (LID) had been a science fiction in 1970's but today; it has been deployed in practical usage. The prosodic features of a speech are relatively simpler in their structure and are accredited to be very affective in some Language Recognition (LR) or LID tasks; irrespective of these features to be biased on numerous factors, as speaker's way of speaking, the culture and background of speaker. The book includes a series of experiments on several speech corpus with different classification or/and identification technique. At the end of each chapter, few review questions have been included and at the verge of the book, a short list of projects for research scholars has been mentioned in addition to a set of MCQs and Important questions. This book motivates for developing a multilingual LID system which can be widely used for betterment of mankind, particularly in the fields of Intelligence Police/Military) services and medical care. In an overview, we may assert that the book explores various experimental datasets, for, performance analysis of LID system with News speech and Natural Conversation speech; Joint Factor Analysis for LR on prosodic featured models and for automatic LID using i-Vector based prosodic system.

著者について

An alumnus of the prestigious Manipal Group, Niraj Singh, hails from the North Eastern states of India and is a research scholar  in the field of digital communication at Vivekananda Global University, Jaipur. He has presented more than Twenty Five Research Articles in various International and National Conferences or Journals. With diversified academic interests, he is a Business Law Graduate from Annamalai University, Chennai and also holds other Diploma Degrees in Journalism, Mass Communication, Import-Export Management. He is also a member of various technical societies like, IEEE, IEEESPS, UACEE, IAENG, ACM, OSI, MMA and others.

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