Text and speech corpora for training a tale telling robot have been designed, recorded and annotated. The aim of these corpora is to study expressive storytelling behaviour, and to help in designing expressive prosodic and co-verbal variations for the artificial storyteller). A set of 89 children tales in French serves as a basis for this work. The tales annotation principles and scheme are described, together with the corpus description in terms of coverage and inter-annotator agreement. Automatic analysis of a new tale with the help of this corpus and machine learning is discussed. Metrics for evaluation of automatic annotation methods are discussed. A speech corpus of about 1 hour, with 12 tales has been recorded and aligned and annotated. This corpus is used for predicting expressive prosody in children tales, above the level of the sentence.

Unit selection text-to-speech systems currently produce very natural synthesized phrases by concatenating speech segments from a large database. Recently, increasing demand for designing high quality voices with less data has created need for further optimization of the textual corpus recorded by the speaker. This corpus is traditionally the result of a condensation process: sentences are selected from a reference corpus, using an optimization algorithm (generally greedy) guided by the coverage rate of classic units (diphones, triphones, words…). Such an approach is, however, strongly constrained by the finite content of the reference corpus, providing limited language possibilities. To gain flexibility in the optimization process, in this paper, we introduce a new corpus building procedure based on sentence construction rather than sentence selection. Sentences are generated using Finite State Transducers, assisted by a human operator and guided by a new frequency-weighted coverage criterion based on Vocalic Sandwiches. This semi-automatic process requires time-consuming human intervention but seems to give access to much denser corpora, with a density increase of 30 to 40% for a given coverage rate.

The EVALDA/EvaSy project is dedicated to the evaluation of text-to-speech synthesis systems for the French language. It is subdivided into four components: evaluation of the grapheme-to-phoneme conversion module (Boula de Mareüil et al., 2005), evaluation of prosody (Garcia et al., 2006), evaluation of intelligibility, and global evaluation of the quality of the synthesised speech. This paper reports on the key results of the intelligibility and global evaluation of the synthesised speech. It focuses on intelligibility, assessed on the basis of semantically unpredictable sentences, but a comparison with absolute category rating in terms of e.g. pleasantness and naturalness is also provided. Three diphone systems and three selection systems have been evaluated. It turns out that the most intelligible system (diphone-based) is far from being the one which obtains the best mean opinion score.

This paper reports on prosodic evaluation in the framework of the EVALDA/EvaSy project for text-to-speech (TTS) evaluation for the French language. Prosody is evaluated using a prosodic transplantation paradigm. Intonation contours generated by the synthesis systems are transplanted on a common segmental content. Both diphone based synthesis and natural speech are used. Five TTS systems are tested along with natural voice. The test is a paired preference test (with 19 subjects), using 7 sentences. The results indicate that natural speech obtains consistently the first rank (with an average preference rate of 80%), followed by a selection based system (72%) and a diphone based system (58%). However, rather large variations in judgements are observed among subjects and sentences, and in some cases synthetic speech is preferred to natural speech. These results show the remarkable improvement achieved by the best selection based synthesis systems in terms of prosody. In this way; a new paradigm for evaluation of the prosodic component of TTS systems has been successfully demonstrated.