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Development and Utility of Automatic Language Processing Technologies. Volume 2
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In: DTIC (2014)
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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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Speaker Clustering for a Mixture of Singing and Reading (Preprint)
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In: DTIC (2012)
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Open-Source Multi-Language Audio Database for Spoken Language Processing Applications
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In: DTIC (2012)
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Speech Processing in Realistic Battlefield Environments (Le Traitement de la Parole en Environnement de Combat Realiste)
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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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Listener Detection of Talker Stress in Low-Rate Coded Speech
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In: DTIC (2008)
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Multimodal Meeting Capture and Understanding with the CALO Meeting Assistant
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In: DTIC (2007)
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Speaker Adaptation of Language Models for Automatic Dialog Act Segmentation of Meetings
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In: DTIC (2007)
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Leveraging Multimodal Redundancy for Dynamic Learning, with SHACER - a Speech and HAndwriting reCognizER
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In: DTIC (2007)
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Speaker Segmentation and Clustering Using Gender Information
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In: DTIC (2006)
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Multilingual Phoneme Models for Rapid Speech Processing System Development
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In: DTIC (2006)
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Human Language Technology: Opportunities and Challenges
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In: DTIC (2005)
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Automatic Multi-Language Phonetic Transcribing System
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In: DTIC AND NTIS (2002)
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Activity Detection for Information Access to Oral Communication
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In: DTIC (2001)
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Clustering of Context Dependent Speech Units for Multilingual Speech Recognition
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In: DTIC (2000)
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Speech Intelligibility of Native and Non-Native Speech
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In: DTIC (2000)
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Speech Recognition by Goats, Wolves, Sheep and Non-Natives
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In: DTIC (2000)
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Abstract:
This paper gives an overview of current understanding of acoustic-phonetic issues arising when trying to recognize speech from non-native speakers. Regional accents can be modeled by systematic shifts in pronunciation. These can often better be represented by multiple models, than by pronunciation variants in the dictionary. The problem of non-native speech is much more difficult because it is influenced both by native and spoken language, making a multi-model approach inappropriate. It is also characterized by a much higher speaker variability due to different levels of proficiency. A few language-pair specific rules describing the prototyical nativised pronunciation was found to be useful both in general speech recognition as in dedicated applications. However, due to the nature of the errors and the mappings, non-native speech recognition will remain inherently much harder. Moreover, the trend in speech recognition towards more detailed modeling is counterproductive for the recognition of non-natives. ; Presented at the Information Systems Technology Panel (IST) Tutorial and Workshop held in Leusden, The Netherlands, 13-14 September 1999. This article is from ADA387529 Multi-Lingual Interoperability in Speech Technology (l'Interoperabilite multilinguistique dans la technologie de la parole)
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Keyword:
*SPEECH RECOGNITION; ACOUSTIC-PHONETIC; BELGIUM; COMPONENT REPORTS; DICTIONARIES; FOREIGN REPORTS; GOATS; LANGUAGE; MODELS; NATO FURNISHED; PROFICIENCY; SHEEP; SHIFTING; SPEECH; SYMPOSIA; VARIATIONS; Voice Communications
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URL: http://www.dtic.mil/docs/citations/ADP010378 http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADP010378
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How Foreign are 'Foreign' Speech Sounds? Implications for Speech Recognition and Speech Synthesis
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In: DTIC (2000)
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