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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)
Abstract: In this study, we propose a speaker clustering algorithm based on reading and singing speech samples for each speaker. As a speaking style, singing introduces changes in the time-frequency structure of a speaker s voice. The purpose of this study is to introduce advancements into speech systems such as speech indexing and retrieval which improve robustness to intrinsic variations in speech production. Clustering is performed within a GMM mean supervector space. The proposed method includes two stages: first, initial clusters are obtained using traditional clustering techniques such as k-means, and hierarchical. Next, each cluster is refined in a PLDA subspace resulting in a more speaker dependent representation that is less sensitive to speaking style. The proposed algorithm improves the average clustering accuracy of the k-means baseline by +9.3% absolute. ; The original document contains color images. This paper was accepted for publication in the Proceedings of Interspeech, Portland, Oregon, 9-13 Sept-2012.
Keyword: *INFORMATION RETRIEVAL; *SPEECH RECOGNITION; ALGORITHMS; BASE LINES; CLUSTERING; FREQUENCY; INDEXES; MEAN; MIXTURES; NOISE(SOUND); PE35885G; READING; SAMPLING; SPEAKER CLUSTERING; SPEAKER INDEXING; SPEECH; SPEECH PRODUCTION; SYMPOSIA; Theoretical Mathematics; Voice Communications; WUAFRL3188BAAE
URL: http://www.dtic.mil/docs/citations/ADA568317
http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA568317
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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)
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