Manual annotation of speech corpora is expensive in both human resources and time. Furthermore, recognizing affects in spontaneous, non acted speech presents a challenge for humans and machines. The aim of the present study is to automatize the labeling of hesitant speech as a marker of expressed uncertainty. That is why, the NCCFr-corpus was manually annotated for ‘degree of hesitation’ on a continuous scale between -3 and 3 and the affective dimensions ‘activation, valence and control’. In total, 5834 chunks of the NCCFr-corpus were manually annotated. Acoustic analyses were carried out based on these annotations. Furthermore, regression models were trained in order to allow automatic prediction of hesitation for speech chunks that do not have a manual annotation. Preliminary results show that the number of filled pauses as well as vowel duration increase with the degree of hesitation, and that automatic prediction of the hesitation degree reaches encouraging RMSE results of 1.6.

This paper describes the neural machine translation (NMT) systems of the LIUM Laboratory developed for the French↔German news translation task of the Fourth Conference onMachine Translation (WMT 2019). The chosen language pair is included for the first time in the WMT news translation task. We de-scribe how the training and the evaluation data was created. We also present our participation in the French↔German translation directions using self-attentional Transformer networks with small and big architectures.

Les apprenants français de l’allemand ont des difficultés à produire la fricative palatale sourde allemande /ç/ (Ich-Laut) et ont tendance à la remplacer par la fricative post-alvéolaire /S/. Nous nous demandons si avec des mesures acoustiques ces imprécisions de production peuvent être quantifiées d’une manière plus objective. Deux mesures acoustiques ont été examinées afin de distinguer au mieux /S/ et /ç/ dans un contexte VC en position finale de mot dans des productions de locuteurs germanophones natifs. Elles servent ensuite à quantifier les difficultés de production des apprenants français. 285 tokens de 20 locuteurs natifs et 20 locuteurs L2 ont été analysés. Les mesures appliquées sont le centre de gravité spectral et des rapports d’intensité par bande de fréquence. Sur les productions de locuteurs natifs, les résultats montrent que la mesure la plus fiable pour distinguer acoustiquement /S/ et /ç/ est le ratio d’intensité entre fréquences hautes (4-7 kHz) et basses (1-4 kHz). Les mesures confirment également les difficultés de production des locuteurs natifs français.

The French Learners Audio Corpus of German Speech (FLACGS) was created to compare German speech production of German native speakers (GG) and French learners of German (FG) across three speech production tasks of increasing production complexity: repetition, reading and picture description. 40 speakers, 20 GG and 20 FG performed each of the three tasks, which in total leads to approximately 7h of speech. The corpus was manually transcribed and automatically aligned. Analysis that can be performed on this type of corpus are for instance segmental differences in the speech production of L2 learners compared to native speakers. We chose the realization of the velar nasal consonant engma. In spoken French, engma does not appear in a VCV context which leads to production difficulties in FG. With increasing speech production complexity (reading and picture description), engma is realized as engma + plosive by FG in over 50% of the cases. The results of a two way ANOVA with unequal sample sizes on the durations of the different realizations of engma indicate that duration is a reliable factor to distinguish between engma and engma + plosive in FG productions compared to the engma productions in GG in a VCV context. The FLACGS corpus allows to study L2 production and perception.