Creating and exploiting multimodal annotated corpora

International audienceThe paper presents a project of the Laboratoire Parole et Langage which aims at collecting, annotating and exploiting a corpus of spoken French in a multimodal perspective. The project directly meets the present needs in linguistics where a growing number of researchers become aware of the fact that a theory of communication which aims at describing real interactions should take into account the complexity of these interactions. However, in order to take into account such a complexity, linguists should have access to spoken corpora annotated in different fields. The paper presents the annotation schemes used in phonetics, morphology and syntax, prosody, gestuality at the LPL together with the type of linguistic description made from the annotations seen in two examples

Semi-Supervised Speech Emotion Recognition with Ladder Networks

Speech emotion recognition (SER) systems find applications in various fields such as healthcare, education, and security and defense. A major drawback of these systems is their lack of generalization across different conditions. This problem can be solved by training models on large amounts of labeled data from the target domain, which is expensive and time-consuming. Another approach is to increase the generalization of the models. An effective way to achieve this goal is by regularizing the models through multitask learning (MTL), where auxiliary tasks are learned along with the primary task. These methods often require the use of labeled data which is computationally expensive to collect for emotion recognition (gender, speaker identity, age or other emotional descriptors). This study proposes the use of ladder networks for emotion recognition, which utilizes an unsupervised auxiliary task. The primary task is a regression problem to predict emotional attributes. The auxiliary task is the reconstruction of intermediate feature representations using a denoising autoencoder. This auxiliary task does not require labels so it is possible to train the framework in a semi-supervised fashion with abundant unlabeled data from the target domain. This study shows that the proposed approach creates a powerful framework for SER, achieving superior performance than fully supervised single-task learning (STL) and MTL baselines. The approach is implemented with several acoustic features, showing that ladder networks generalize significantly better in cross-corpus settings. Compared to the STL baselines, the proposed approach achieves relative gains in concordance correlation coefficient (CCC) between 3.0% and 3.5% for within corpus evaluations, and between 16.1% and 74.1% for cross corpus evaluations, highlighting the power of the architecture

Creating Comparable Multimodal Corpora for Nordic Languages

Proceedings of the 18th Nordic Conference of Computational Linguistics NODALIDA 2011. Editors: Bolette Sandford Pedersen, Gunta Nešpore and Inguna Skadiņa. NEALT Proceedings Series, Vol. 11 (2011), 153-160. © 2011 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/16955

Timing Relationships between Speech and Co-Verbal Gestures in Spontaneous French

International audienceSeveral studies have described the links between gesture and speech in terms of timing, most of them concentrating on the production of hand gestures during speech or during pauses (Beattie & Aboudan, 1994; Nobe, 2000). Other studies have focused on the anticipation, synchronization or delay of gestures regarding their co-occurrence with speech (Schegloff, 1984; McNeill, 1992, 2005; Kipp, 2003; Loehr, 2004; Chui, 2005; Kida & Faraco, 2008; Leonard and Cummins, 2009) and we would like to participate in the debate in the present paper. We studied the timing relationships between iconic gestures and their lexical affiliates (Kipp, Neff et al., 2001) in a corpus of French conversational speech involving 6 speakers and annotated both in Praat (Boersma & Weenink, 2009) and Anvil (Kipp, 2001). The timing relationships we observed concerned the position of the gesture stroke as compared to that of the lexical affiliate and the Intonation Phrase, as well as the position of the gesture Phrase as regards that of the Intonation Phrase. The main results show that although gesture and speech are co-occurring, gestures generally start before the related speech segment

Timing Relationships between Speech and Co-Verbal Gestures in Spontaneous French

International audienceSeveral studies have described the links between gesture and speech in terms of timing, most of them concentrating on the production of hand gestures during speech or during pauses (Beattie & Aboudan, 1994; Nobe, 2000). Other studies have focused on the anticipation, synchronization or delay of gestures regarding their co-occurrence with speech (Schegloff, 1984; McNeill, 1992, 2005; Kipp, 2003; Loehr, 2004; Chui, 2005; Kida & Faraco, 2008; Leonard and Cummins, 2009) and we would like to participate in the debate in the present paper. We studied the timing relationships between iconic gestures and their lexical affiliates (Kipp, Neff et al., 2001) in a corpus of French conversational speech involving 6 speakers and annotated both in Praat (Boersma & Weenink, 2009) and Anvil (Kipp, 2001). The timing relationships we observed concerned the position of the gesture stroke as compared to that of the lexical affiliate and the Intonation Phrase, as well as the position of the gesture Phrase as regards that of the Intonation Phrase. The main results show that although gesture and speech are co-occurring, gestures generally start before the related speech segment

Final FLaReNet deliverable: Language Resources for the Future - The Future of Language Resources

Language Technologies (LT), together with their backbone, Language Resources (LR), provide an essential support to the challenge of Multilingualism and ICT of the future. The main task of language technologies is to bridge language barriers and to help creating a new environment where information flows smoothly across frontiers and languages, no matter the country, and the language, of origin. To achieve this goal, all players involved need to act as a community able to join forces on a set of shared priorities. However, until now the field of Language Resources and Technology has long suffered from an excess of individuality and fragmentation, with a lack of coherence concerning the priorities for the field, the direction to move, not to mention a common timeframe. The context encountered by the FLaReNet project was thus represented by an active field needing a coherence that can only be given by sharing common priorities and endeavours. FLaReNet has contributed to the creation of this coherence by gathering a wide community of experts and making them participate in the definition of an exhaustive set of recommendations

Exploiting `Subjective' Annotations

Many interesting phenomena in conversation can only be annotated as a subjective task, requiring interpretative judgements from annotators. This leads to data which is annotated with lower levels of agreement not only due to errors in the annotation, but also due to the differences in how annotators interpret conversations. This paper constitutes an attempt to find out how subjective annotations with a low level of agreement can profitably be used for machine learning purposes. We analyse the (dis)agreements between annotators for two different cases in a multimodal annotated corpus and explicitly relate the results to the way machine-learning algorithms perform on the annotated data. Finally we present two new concepts, namely `subjective entity' classifiers resp. `consensus objective' classifiers, and give recommendations for using subjective data in machine-learning applications.\u