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1
An auditory saliency pooling-based LSTM model for speech intelligibility classification
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2
A Comparison of Open-Source Segmentation Architectures for Dealing with Imperfect Data from the Media in Speech Synthesis
Gallardo Antolín, Ascensión; Montero, Juan Manuel; King, Simon. - : International Speech Communication Association, 2014
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3
A satisfaction-based model for affect recognition from conversational features in spoken dialog systems
In: Speech communication. - Amsterdam [u.a.] : Elsevier 55 (2013) 7, 825-840
OLC Linguistik
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4
I Feel You: The Design and Evaluation of a Domotic Affect-Sensitive Spoken Conversational Agent
Lutfi, Syaheerah Lebai; Fernández-Martínez, Fernando; Lorenzo-Trueba, Jaime. - : Molecular Diversity Preservation International (MDPI), 2013
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5
Automatic categorization for improving Spanish into Spanish Sign Language machine translation
In: Computer speech and language. - Amsterdam [u.a.] : Elsevier 26 (2012) 3, 149-167
BLLDB
OLC Linguistik
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6
Speaker diarization based on intensity channel contribution
In: Institute of Electrical and Electronics Engineers. IEEE transactions on audio, speech and language processing. - New York, NY : Inst. 19 (2011) 4, 754-761
BLLDB
OLC Linguistik
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7
Analysis of Statistical Parametric and Unit Selection Speech Synthesis Systems Applied to Emotional Speech
In: ISSN: 0167-6393 ; EISSN: 1872-7182 ; Speech Communication ; https://hal.archives-ouvertes.fr/hal-00627926 ; Speech Communication, Elsevier : North-Holland, 2010, 52 (5), pp.394. ⟨10.1016/j.specom.2009.12.007⟩ (2010)
Abstract: International audience ; We have applied two state-of-the-art speech synthesis techniques (unit selection and HMM-based synthesis) to the synthesis of emotional speech. A series of carefully designed perceptual tests to evaluate speech quality, emotion identification rates and emotional strength were used for the six emotions which we recorded -, , ,, , . For the HMM-based method, we evaluated spectral and source components separately and identified which components contribute to which emotion.Our analysis shows that, although the HMM method produces significantly better neutral speech, the two methods produce emotional speech of similar quality, except for emotions having context-dependent prosodic patterns. Whilst synthetic speech produced using the unit selection method has better emotional strength scores than the HMM-based method, the HMM-based method has the ability to manipulate the emotional strength. For emotions that are characterized by both spectral and prosodic components, synthetic speech using unit selection methods was more accurately identified by listeners. For emotions mainly characterized by prosodic components, HMM-based synthetic speech was more accurately identified. This finding differs from previous results regarding listener judgements of speaker similarity for neutral speech. We conclude that unit selection methods require improvements to prosodic modeling and that HMM-based methods require improvements to spectral modeling for emotional speech. Certain emotions cannot be reproduced well by either method.
Keyword: Emotional speech synthesis; HMM-based synthesis; unit selection
URL: https://hal.archives-ouvertes.fr/hal-00627926/file/PEER_stage2_10.1016%252Fj.specom.2009.12.007.pdf
https://hal.archives-ouvertes.fr/hal-00627926/document
https://hal.archives-ouvertes.fr/hal-00627926
https://doi.org/10.1016/j.specom.2009.12.007
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8
Analysis of statistical parametric and unit selection speech synthesis systems applied to emotional speech
In: Speech communication. - Amsterdam [u.a.] : Elsevier 52 (2010) 5, 394-404
BLLDB
OLC Linguistik
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9
Speech to sign language translation system for Spanish
In: Speech communication. - Amsterdam [u.a.] : Elsevier 50 (2008) 11-12, 1009-1020
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OLC Linguistik
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10
Knowledge-combining methodology for dialogue design in spoken language systems
In: International journal of speech technology. - Boston, Mass. [u.a.] : Kluwer Acad. Publ. 8 (2005) 1, 45-66
BLLDB
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11
Selection of the most significant parameters for duration modelling in a Spanish text-to-speech system using neural networks
In: Computer speech and language. - Amsterdam [u.a.] : Elsevier 16 (2002) 2, 183-203
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