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How Autism Affects Speech Understanding in Multitalker Environments
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In: DTIC (2014)
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| 23 |
Development and Utility of Automatic Language Processing Technologies. Volume 2
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In: DTIC (2014)
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| 24 |
Biological Information Processing in Single Microtubules
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In: DTIC (2014)
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| 27 |
Cortical Signatures of Heard and Imagined Speech Envelopes
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In: DTIC (2013)
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| 28 |
How Autism Affects Speech Understanding in Multitalker Environments
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In: DTIC (2013)
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| 29 |
Computational Modeling of Emotions and Affect in Social-Cultural Interaction
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In: DTIC (2013)
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| 30 |
What's Wrong With Automatic Speech Recognition (ASR) and How Can We Fix It?
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In: DTIC (2013)
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| 31 |
A Submodularity Framework for Data Subset Selection
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In: DTIC (2013)
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| 32 |
KEYNOTE 2 : Rebuilding the Tower of Babel - Better Communication with Standards
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In: DTIC (2013)
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| 33 |
Role of Autism Susceptibility Gene, CNTNAP2, in Neural Circuitry for Vocal Communication
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In: DTIC (2013)
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| 34 |
Raummetaphern als Brücke zwischen Internetwahrnehmung und Internetkommunikation
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In: 04-4 ; sofia-Diskussionsbeiträge zur Institutionenanalyse ; 32 (2013)
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| 35 |
The Representation and Processing of Tense, Aspect & Voice across Verbal Elements in English
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In: DTIC (2012)
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| 36 |
A Spoken Dialogue System for Command and Control
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In: DTIC (2012)
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| 37 |
Distributed Estimation in Sensor Networks with Imperfect Model Information: An Adaptive Learning-Based Approach
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In: DTIC (2012)
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| 38 |
Speech Synthesis Using Perceptually Motivated Features
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In: DTIC (2012)
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| 39 |
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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| 40 |
Machine Recognition vs Human Recognition of Voices
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In: DTIC (2012)
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