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Acoustic analysis and measurements of distorted speech in the NZ population
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Acoustic analysis and measurements of distorted speech in the NZ population
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Acoustic analysis and measurements of distorted speech in the NZ population
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Acoustic analysis and computerized reconstruction of speech in laryngectomised individuals
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Acoustic analysis and computerized reconstruction of speech in laryngectomised individuals
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Acoustic analysis and computerized reconstruction of speech in laryngectomised individuals
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| 8 |
The Voice Conversion Challenge, 2016: multidimensional scaling (MDS) listening test results ...
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Unkn Unknown. - : University of Edinburgh. School of Informatics. Centre for Speech Technology Research, 2016
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| 9 |
Cascading Oscillators in Decoding Speech: Reflection of a Cortical Computation Principle
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Investigation of Back-off Based Interpolation Between Recurrent Neural Network and N-gram Language Models (Author's Manuscript)
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The interaction of long-term voice quality with the realisation of focus ; Speech Prosody 2016
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Bionic voice (pilot study) : natural speech restoration for voice impaired individuals
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Bionic voice (pilot study) : natural speech restoration for voice impaired individuals
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Bionic voice (pilot study) : natural speech restoration for voice impaired individuals
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| 16 |
Robust Speech Processing & Recognition: Speaker ID, Language ID, Speech Recognition/Keyword Spotting, Diarization/Co-Channel/Environmental Characterization, Speaker State Assessment
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In: DTIC (2015)
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The Effects of Student Narration in College Engineering Classes
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Study of Discussion Record Analysis Using Temporal Data Crystallization and Its Application to TV Scene Analysis
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In: DTIC (2015)
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Respirator Speech Intelligibility Testing with an Experienced Speaker
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In: DTIC (2015)
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A Novel Scheme for Speaker Recognition Using a Phonetically-Aware Deep Neural Network
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In: DTIC (2014)
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Abstract:
We propose a novel framework for speaker recognition in which extraction of sufficient statistics for the state-of-the-art i-vector model is driven by a deep neural network (DNN) trained for automatic speech recognition (ASR). Specifically, the DNN replaces the standard Gaussian mixture model (GMM) to produce frame alignments. The use of an ASR-DNN system in the speaker recognition pipeline is attractive as it integrates the information from speech content directly into the statistics, allowing the standard backends to remain unchanged. Improvement from the proposed framework compared to a state-of-the-art system are of 30% relative at the equal error rate when evaluated on the telephone conditions from the 2012 NIST speaker recognition evaluation (SRE). The proposed framework is a successful way to efficiently leverage transcribed data for speaker recognition, thus opening up a wide spectrum of research directions.
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Keyword:
*SPEECH RECOGNITION; DEEP NEURAL NETWORK; NEURAL NETS; STATISTICS; Voice Communications
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URL: http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA613971
http://www.dtic.mil/docs/citations/ADA613971
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