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Hits 61 – 74 of 74

61
INVESTIGATING ARTICULATORY SETTING—PAUSES, READY POSITION, AND REST—USING REAL-TIME MRI
In: http://www-scf.usc.edu/%7Evramanar/Articulatory_Setting_Interspeech_2010_v8_reviewed.pdf
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62
Modeling emotion expression and perception behavior in auditive emotion evaluation
In: http://www.isca-speech.org/archive/sp2006/papers/sp06_071.pdf
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63
An Exploratory Study of Emotional Speech Production using Functional Data Analysis Techniques
In: http://sail.usc.edu/publications/issp2006-emotion-mri-fda.pdf
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64
The Twins Corpus of Museum Visitor Questions
In: http://www.lrec-conf.org/proceedings/lrec2012/pdf/595_Paper.pdf
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65
Data-Driven Unsupervised Adaptation of Acoustic-Prosodic Models
In: http://www.isca-speech.org/archive/sp2008/papers/sp08_161.pdf
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66
Detection of Non-Native Named Entities Using Prosodic Features for Improved Speech Recognition and Translation
In: http://sail.usc.edu/publications/vivek_multiling-final-draft.pdf
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67
Language-Adaptive Persian Speech Recognition
In: http://biron.usc.edu/~snaveen/papers/LAAM.pdf
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68
ISCA Archive Use of Model Transformations for Distributed Speech Recognition
In: http://isca-speech.org/archive_open/archive_papers/adaptation/adap_113.pdf
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69
Tactical language training system: An interim report
In: http://alelo.com/files/ITS04-tactical_language.pdf
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70
Pronunciation variations of Spanish-accented English spoken by young children
In: http://www.seas.ucla.edu/spapl/paper/hong_euro05.pdf
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71
Acoustic-Prosodic, Turn-taking, and Language Cues in Child-Psychologist Interactions for Varying Social Demand
In: http://sail.usc.edu/%7Edbone/Bone_prosodyLanguage_socialDemand_IS2013.pdf
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72
An acoustic study of emotions expressed in speech
In: http://graphics.cs.uh.edu/publication/pub/ICSLP04_acousticStudy.pdf
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73
Modeling and Automating Detection of Errors in Arabic Language Learner Speech
In: http://sail.usc.edu/publications/paper_pron.pdf
Abstract: Human tutors, in dealing with non-native speakers, draw from their knowledge of common learner mistakes to understand learner speech and offer effective corrective advice. In this paper we present our work towards embedding some of this knowledge in the speech recognition and learner speech error detection subsystems of the Tactical Language Training System (TLTS). We discuss the implementation and effectiveness of our methodology which uses a combination of rule based and probabilistic models derived from linguistic knowledge about the target language and annotated speech to identify potential learner errors, detect them using ASR and to provide the user with corrective feedback based on error severity and factors such as the learner history. Evaluation results show that our system can provide effective feedback to the learner with good accuracy.
URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.115.1103
http://sail.usc.edu/publications/paper_pron.pdf
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74
Liquids In Tamil
In: http://www.asel.udel.edu/icslp/cdrom/vol2/984/a984.pdf
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