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Amit Juneja

from Kensington, MD
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Related to:
Dipak Narula, 66Kusum Narula, 73Vinod K Narula, 79
Connected to:
Ela H Juneja, 44Krishan L Juneja, 87Rajaraghuvir M Juneja, 65Sangeet Juneja, 49Afshan T Junejo, 49Roohi A Junejo, 61Sadiq Junejo, 50Jacqueline M Junek, 62Ella Junel, 47Artis Junell, 29

Amit Juneja Phones & Addresses

  • Kensington, MD

Resumes

Resumes

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Amit Juneja

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Associate Director At Fortigent, Llc

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Position:
Associate Director at Fortigent, LLC
Location:
Washington D.C. Metro Area
Industry:
Financial Services
Work:
Fortigent, LLC since Oct 2010
Associate Director
Fortigent, LLC Oct 2008 - Oct 2010
Senior Lead Analyst
Fortigent, LLC Mar 2008 - Oct 2008
Senior Analyst
Fortigent, LLC Mar 2007 - Mar 2008
Analyst
AXA Advisors Apr 2006 - Mar 2007
Financial Advisor
Education:
University of Maryland College Park 2001 - 2005
Bachelors, Economics & Criminology
Skills:
Retirement Planning
Series 7
401k
Life Insurance
Finance
Uniform Combined State Law
Investments
Portfolio Management
Bloomberg
Equities
Wealth Management
Mutual Funds
Investment Advisory
Asset Allocation
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Amit Juneja

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Location:
United States
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Amit Juneja

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Location:
United States
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Business Records

Name / Title
Company / Classification
Phones & Addresses
Amit Juneja
SIGNOMICS LLC

Publications

Us Patents

System And Method For Automatic Speech Recognition From Phonetic Features And Acoustic Landmarks

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US Patent:
20060212296, Sep 21, 2006
Filed:
Mar 17, 2005
Appl. No.:
11/081507
Inventors:
Carol Espy-Wilson - Washington DC, US
Amit Juneja - Roxbury MA, US
International Classification:
G10L 15/04
US Classification:
704254000
Abstract:
A probabilistic framework for acoustic-phonetic automatic speech recognition organizes a set of phonetic features into a hierarchy consisting of a broad manner feature sub-hierarchy and a fine phonetic feature sub-hierarchy. Each phonetic feature of said hierarchy corresponds to a set of acoustic correlates and each broad manner feature of said broad manner feature sub-hierarchy is further associated with a corresponding set of acoustic landmarks. A pattern recognizer is trained from a knowledge base of phonetic features and corresponding acoustic correlates. Acoustic correlates are extracted from a speech signal and are presented to the pattern recognizer. Acoustic landmarks are identified and located from broad manner classes classified by the pattern recognizer. Fine phonetic features are determined by the pattern recognizer at and around the acoustic landmarks. The determination of fine phonetic features may be constrained by a pronunciation model. The most probable feature bundles corresponding to words and sentences are those that maximize the joint a posteriori probability of the fine phonetic features and corresponding acoustic landmarks. When the hierarchy is organized as a binary tree, binary classifiers such as Support Vector Machines can be used in the pattern classifier and the outputs thereof can be converted probability measures which, in turn may be used in the computation of the aforementioned joint probability of fine phonetic features and corresponding landmarks.
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Amit Juneja from Kensington, MD Get Report