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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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| 32 |
Familiar Speaker Recognition
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In: DTIC (2012)
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Abstract:
Speaker recognition by machines can be quite good for large groups as demonstrated in NIST speaker recognition evaluations. However, speaker recognition by machines can be fragile in changing environments. This research examines how robust humans are at recognizing familiar speakers in changing environments. The short-term goal of the research was to learn what frequency information is important for the recognition of familiar speakers by masking out certain frequency information. The long-term goal of the research is to use this information to develop more robust speaker recognition features. The authors used additive speech-shaped noise (LTASS) to degrade particular frequency regions of the speech signal. This way, the signal still sounded natural and the performance of listeners could be tied to the degradation of particular frequencies. If the performance decreased when a set of frequencies was masked by an interfering signal, it would indicate that the frequency range was important. The main conclusion of the research is that the distributions of the Normal Hearing and Hearing Deficit groups were statistically different for each listening condition, both for the performance values and the average elapsed time. Additional analysis is being performed to identify factors that may impact a listener's ability to identify a person's identity. All the bandlimited noise conditions resulted in lower performance compared to the clean (no noise) conditions. 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, p4237-4240, 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 LISTENERS; *PERFORMANCE(HUMAN); *PSYCHOACOUSTICS; *SIGNAL TO NOISE RATIO; *SPEAKER IDENTIFICATION; *SPEAKER RECOGNITION; *SPEECH RECOGNITION; *VOICE COMMUNICATIONS; Acoustics; AUDITORY SIGNALS; BACKGROUND NOISE; CUES(STIMULI); FEMALES; HEARING DEFICIT GROUP; IDENTIFICATION; LISTENING EXPERIMENTS; MALES; NORMAL HEARING GROUP; Psychology; SPEAKER CUES; SPEAKER FAMILIARITY; SPEECH SIGNAL DEGRADATION; SPEECH SIGNALS; SPEECH-SHAPED ADDITIVE NOISE; STATISTICAL ANALYSIS; SYMPOSIA; TRAINING; Voice Communications; VOICE RECOGNITION BY HUMANS
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URL: http://www.dtic.mil/docs/citations/ADA568901
http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA568901
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| 33 |
Machine Recognition vs Human Recognition of Voices
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In: DTIC (2012)
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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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