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Machine Recognition vs Human Recognition of Voices
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In: DTIC (2012)
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Speaker Clustering for a Mixture of Singing and Reading (Preprint)
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In: DTIC (2012)
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Compressed Domain Automatic Level Control Based on ITU-T G.722.2
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In: DTIC (2012)
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Segregation of Whispered Speech Interleaved with Noise or Speech Maskers
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In: DTIC (2011)
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The SRI NIST 2010 Speaker Recognition Evaluation System (PREPRINT)
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In: DTIC (2011)
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Recovering Asynchronous Watermark Tones from Speech
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In: DTIC (2009)
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The Multi-Session Audio Research Project (MARP) Corpus: Goals, Design and Initial Findings
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In: DTIC (2009)
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Long Term Examination of Intra-Session and Inter-Session Speaker Variability
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In: DTIC (2009)
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Perturbation and Pitch Normalization as Enhancements to Speaker Recognition
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In: DTIC (2009)
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Long Term Examination of Intra-Session and Inter-Session Speaker Variability
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In: DTIC (2009)
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Automating Convoy Training Assessment to Improve Soldier Performance
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In: DTIC (2008)
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Odds of Successful Transfer of Low-level Concepts: A Key Metric for Bidirectional Speech-to-Speech Machine Translation in DARPA's TRANSTAC Program
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In: DTIC (2008)
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Iterated Class-Specific Subspaces for Speaker-Dependent Phoneme Classification
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In: DTIC (2008)
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Listener Detection of Talker Stress in Low-Rate Coded Speech
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In: DTIC (2008)
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Comparing Evaluation Metrics for Sentence Boundary Detection
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In: DTIC (2007)
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Using Prosody for Automatic Sentence Segmentation of Multi-Party Meetings
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In: DTIC (2006)
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Abstract:
We explore the use of prosodic features beyond pauses, including duration, pitch, and energy features, for automatic sentence segmentation of ICSI meeting data. We examine two different approaches to boundary classification: score-level combination of independent language and prosodic models using HMMs, and feature-level combination of models using a boosting-based method (BoosTexter). We report classification results for reference word transcripts as well as for transcripts from a state-of-the-art automatic speech recognizer (ASR). We also compare results using the lexical model plus a pause-only prosody model, versus results using additional prosodic features. Results show that (1) information from pauses is important, including pause duration both at the boundary and at the previous and following word boundaries; (2) adding duration, pitch, and energy features yields significant improvement over pause alone; (3) the integrated boosting-based model performs better than the HMM for ASR conditions; (4) training the boosting-based model on recognized words yields further improvement. ; Presented at the International Conference on Text, Speech, and Dialogue (9th) (TSD 2006) held in Brno, Czech Republic on 11-15 Sep 2006. Published in the Proceedings of the International Conference on Text, Speech, and Dialogue (9th), 2006. Sponsored in part by National Science Foundation contract no. IIS-0121396.
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Keyword:
*BOUNDARIES; *MODELS; *PROSODIC FEATURES; *PROSODY; *SENTENCE SEGMENTATION; *SPEECH RECOGNITION; ALGORITHMS; ASR(AUTOMATIC SPEECH RECOGNIZER); AUTOMATIC; BOOSTING; CLASSIFICATION; CLASSIFIERS; Cybernetics; HMM(HIDDEN MARKOV MODELS); LEXICAL FEATURES; Linguistics; MARKOV PROCESSES; PAUSES; SYMPOSIA; TEAMS(PERSONNEL); Voice Communications; WORDS(LANGUAGE)
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URL: http://www.dtic.mil/docs/citations/ADA459015 http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA459015
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A Methodology to Predict Specific Communication Themes from Overall Communication Volume for Individuals and Teams
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In: DTIC (2006)
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Toward an Interagency Language Roundtable Based Assessment of Speech-to-Speech Translation Capabilities
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In: DTIC (2006)
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The Mixer and Transcript Reading Corpora: Resources for Multilingual, Crosschannel Speaker Recognition Research
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In: DTIC (2006)
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