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Insights from the Women in Combat Symposium
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In: DTIC (2013)
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23 |
Automated Extraction and Characterisation of Social Network Data from Unstructured Sources -- An Ontology-Based Approach
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In: DTIC (2013)
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24 |
Interacting with Multi-Robot Systems Using BML
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In: DTIC (2013)
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25 |
Making Semantic Information Work Effectively for Degraded Environments
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In: DTIC (2013)
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26 |
Accelerating Exploitation of Low-grade Intelligence through Semantic Text Processing of Social Media
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In: DTIC (2013)
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28 |
QUT Para at TREC 2012 Web Track: Word Associations for Retrieving Web Documents
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In: DTIC (2012)
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30 |
Phoneme Class Based Adaptation for Mismatch Acoustic Modeling of Distant Noisy Speech (Preprint)
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In: DTIC (2012)
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31 |
Conference Report: Cultural and Linguistic Advancement for Mission Success: Enhancing Language, Regional and Cultural Capabilities Across Whole of Government for an Effective COIN Strategy
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In: DTIC (2012)
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33 |
Machine Recognition vs Human Recognition of Voices
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In: DTIC (2012)
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Abstract:
While automated speaker recognition by machines can be quite good as demonstrated in NIST Speaker Recognition Evaluations, performance can still suffer when environmental conditions, emotions, or recording quality change. This research examines how robust humans are compared with machines at speaker recognition in changing environments. Several data conditions, including short sentences, frequency selective noise, and time-reversed speech were used to test the robustness of human listeners versus machine algorithms. Statistical significance tests were completed on the results and, for under conditions, human speaker recognition was more robust. The strength of the human listeners was especially evident for the challenging case of noise in the 2000-3000 Hz frequency range. Additional analysis was performed to identify factors that may impact a listener's ability to identify a person's identity. For example, the amount of voiced (or unvoiced) speech was examined to see if there was a correlation with how easily a speaker's voice was recognized. Unfortunately, the amount of voiced (or unvoiced) speech did not correlate strongly with how easily a speaker's voice was recognized. Other factors such as fundamental pitch, formant locations, pitch shimmer, pitch jitter, and other modulation measures also are being examined. The original goal of this effort was to discover which frequency bands are most important for the familiar speaker recognition task. This research was a cursory look at what frequency information is important for speaker identification. More listening experiments with better randomization of stimuli and phonetic consideration are required. ; See also ADA561051. Presented at the International Conference on Acoustics, Speech and Signal Processing (37th) (ICASSP 2012) held in Kyoto, Japan, on March 25-30, 2012. Published in the Proceedings of the 37th International Conference on Acoustics, Speech and Signal Processing, p4245-4248, 2012. U.S. Government or Federal Purpose Rights License. The original document contains color images.
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Keyword:
*AUDIO FREQUENCY; *FAMILIAR SPEAKERS; *FREQUENCY BANDS; *HUMAN PERFORMANCE; *LEARNING MACHINES; *MACHINE PERFORMANCE; *PERFORMANCE(ENGINEERING); *PERFORMANCE(HUMAN); *SIGNAL TO NOISE RATIO; *SPEAKER IDENTIFICATION; *SPEAKER RECOGNITION; *SPEECH RECOGNITION; Acoustics; ALGORITHMS; AUDITORY SIGNALS; BACKGROUND NOISE; CO-WORKER IDENTIFICATION; CUES(STIMULI); Cybernetics; HEARING DEFICIT GROUP; HUMAN LISTENERS; IDENTIFICATION; NORMAL HEARING GROUP; Psychology; SIGNAL PROCESSING; SPEAKER CUES; SPEAKER FAMILIARITY; SPEECH SIGNAL DEGRADATION; SPEECH SIGNALS; SPEECH-SHAPED ADDITIVE NOISE; STATISTICAL ANALYSIS; SYMPOSIA; TRAINING; Voice Communications
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URL: http://www.dtic.mil/docs/citations/ADA568903 http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA568903
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34 |
Speaker Clustering for a Mixture of Singing and Reading (Preprint)
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In: DTIC (2012)
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35 |
Applications of Lexical Link Analysis Web Service for Large-Scale Automation, Validation, Discovery, Visualization, and Real-Time Program-Awareness
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In: DTIC (2012)
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36 |
Compressed Domain Automatic Level Control Based on ITU-T G.722.2
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In: DTIC (2012)
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37 |
Integrating Hard and Soft Information Sources for D2D Using Controlled Natural Language
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In: DTIC (2012)
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38 |
SAWUS: Siena's Automatic Wikipedia Update System
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In: DTIC (2012)
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39 |
Aligning Learning Capability with Strategy: A Training Needs Assessment (TNA) Case Study
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In: DTIC (2012)
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40 |
Learning for Microblogs with Distant Supervision: Political Forecasting with Twitter
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In: DTIC (2012)
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