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1
The 2016 NIST Speaker Recognition Evaluation
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2
Cascading Oscillators in Decoding Speech: Reflection of a Cortical Computation Principle
Ghitza,Oded. - 2016
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3
The MITLL NIST LRE 2015 Language Recognition System
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4
Investigation of Back-off Based Interpolation Between Recurrent Neural Network and N-gram Language Models (Author's Manuscript)
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5
Robust Speech Processing & Recognition: Speaker ID, Language ID, Speech Recognition/Keyword Spotting, Diarization/Co-Channel/Environmental Characterization, Speaker State Assessment
In: DTIC (2015)
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6
A Novel Scheme for Speaker Recognition Using a Phonetically-Aware Deep Neural Network
In: DTIC (2014)
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7
VOCALinc
In: DTIC (2014)
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8
How Autism Affects Speech Understanding in Multitalker Environments
In: DTIC (2014)
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9
Development and Utility of Automatic Language Processing Technologies. Volume 2
In: DTIC (2014)
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10
Understanding Tonal Languages
In: DTIC (2013)
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11
Computational Modeling of Emotions and Affect in Social-Cultural Interaction
In: DTIC (2013)
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12
What's Wrong With Automatic Speech Recognition (ASR) and How Can We Fix It?
In: DTIC (2013)
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13
A Submodularity Framework for Data Subset Selection
In: DTIC (2013)
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14
A Spoken Dialogue System for Command and Control
In: DTIC (2012)
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15
Speech Synthesis Using Perceptually Motivated Features
In: DTIC (2012)
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16
Familiar Speaker Recognition
In: DTIC (2012)
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17
Machine Recognition vs Human Recognition of Voices
In: DTIC (2012)
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18
Speaker Clustering for a Mixture of Singing and Reading (Preprint)
In: DTIC (2012)
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19
Open-Source Multi-Language Audio Database for Spoken Language Processing Applications
In: DTIC (2012)
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20
Effects of Speech Intensity on the Callsign Acquisition Test (CAT) and Modified Rhyme Test (MRT) Presented in Noise
In: DTIC (2012)
Abstract: This study sought to evaluate the effect of speech intensity on performance of the Callsign Acquisition Test (CAT) and Modified Rhyme Test (MRT) presented in noise. Fourteen normally hearing listeners performed both tests in 65 dB A white background noise. Speech intensity varied while background noise remained constant to form speech-to-noise ratios. Results showed that CAT recognition scores were significantly higher than MRT scores at the same SNRs; however, the scores from both tests were highly correlated and their relationship for the SNRs tested can be expressed by a simple linear function. The concept of CAT can be easily ported to other languages for testing speech communication under adverse listening conditions. ; Published in Archives of Acoustics, v37 n2, p199-203 (2012),1/20. The original document contains color images.
Keyword: *SPEECH; *VOICE COMMUNICATIONS; ACQUISITION; ADVERSE CONDITIONS; BACKGROUND NOISE; CAT(CALLSIGN ACQUISITION TEST); FUNCTIONS(MATHEMATICS); HEARING; INTENSITY; LANGUAGE; LINEARITY; MRT(MODIFIED RHYME TEST); NOISE; RATIOS; RECOGNITION; SCORING; SIGNAL TO NOISE RATIO; SPEECH INTELLIGIBILITY; SPEECH INTENSITY; SPEECH TRANSMISSION; TEST AND EVALUATION; Voice Communications; WHITE NOISE
URL: http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA564752
http://www.dtic.mil/docs/citations/ADA564752
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