In this paper, we present the application of a novel automatic prosodic labeling methodology for speeding up the manual labeling of the Glissando corpus (Spanish read news items). The methodology is based on the use of soft classification techniques. The output of the automatic system consists on a set of label candidates per word. The number of predicted candidates depends on the degree of certainty assigned by the classifier to each of the predictions. The manual transcriber checks the sets of predictions to select the correct one. We describe the fundamentals of the fuzzy classification tool and its training with a corpus labeled with Sp TOBI labels. Results show a clear coherence between the most confused labels in the output of the automatic classifier and the most confused labels detected in inter-transcriber consistency tests. More importantly, in a preliminary test, the real time ratio of the labeling process was 1:66 when the template of predictions is used and 1:80 when it is not.

In this work we present SAMPLE, a new pronunciation database of Spanish as L2, and first results on the automatic assessment of Non-native prosody. Listen and repeat and read tasks are carried out by native and foreign speakers of Spanish. The corpus has been designed to support comparative studies and evaluation of automatic pronunciation error assessment both at phonetic and prosodic level. Four expert evaluators have annotated utterances with perceptual scores related to prosodic aspects of speech, intelligibility, phonetic quality and global proficiency level in Spanish. From each utterance, we computed several prosodic features and ASR scores. A correlation study over subjective and quantitative measures is carried out. An estimation of the prediction of perceptual scores from speech features is shown.