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1	The IRISA Text-To-Speech System for the Blizzard Challenge 2015
	Alain, Pierre; Chevelu, Jonathan; Guennec, David...
	In: Blizzard Challenge 2015 Workshop ; https://hal.inria.fr/hal-01196168 ; Blizzard Challenge 2015 Workshop, Sep 2015, Berlin, Germany. 4 p., 2 columns (2015)
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2	Adaptive Statistical Utterance Phonetization for French
	Lecorvé, Gwénolé; Lolive, Damien
	In: Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ; https://hal.inria.fr/hal-01109757 ; Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr 2015, Brisbane, Australia. 5 p., 2 columns (2015)
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3	Probabilistic Speaker Pronunciation Adaptation for Spontaneous Speech Synthesis Using Linguistic Features
	Qader, Raheel; Lecorvé, Gwénolé; Lolive, Damien; Sébillot, Pascale
	In: Proceedings of Statistical Language and Speech Processing ; International Conference on Statistical Language and Speech Processing (SLSP) ; https://hal.inria.fr/hal-01181192 ; International Conference on Statistical Language and Speech Processing (SLSP), Nov 2015, Budapest, Hungary. pp.229-241 (2015)
	Abstract: International audience ; Pronunciation adaptation consists in predicting pronunciation variants of words and utterances based on their standard pronunciation and a target style. This is a key issue in text-to-speech as those variants bring expressiveness to synthetic speech, especially when considering a spontaneous style. This paper presents a new pronunciation adaptation method which adapts standard pronunciations to the style of individual speakers in a context of spontaneous speech. Its originality and strength are to solely rely on linguistic features and to consider a probabilistic machine learning framework, namely conditional random fields, to produce the adapted pronunciations. Features are first selected in a series of experiments, then combined to produce the final adaptation method. Backend experiments on the Buckeye conversational English speech corpus show that adapted pronunciations significantly better reflect spontaneous speech than standard ones, and that even better could be achieved if considering alternative predictions.
	Keyword: [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing; [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing; adaptation de la prononciation; caractéristiques linguistiques; champs aléatoires conditionnels; conditional random fields; feature selection; linguistic features; pronunciation adaptation; sélection de caractéristiques; spontaneous speech synthesis; synthèse de la parole spontanée
	URL: https://hal.inria.fr/hal-01181192/file/94490238.pdf https://hal.inria.fr/hal-01181192/document https://hal.inria.fr/hal-01181192
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