Temporal Bayesian Fusion for Affect Sensing: Combining Video, Audio, and Lexical Modalities.

The affective state of people changes in the course of conversations and these changes are expressed externally in a variety of channels, including facial expressions, voice, and spoken words. Recent advances in automatic sensing of affect, through cues in individual modalities, have been remarkable; yet emotion recognition is far from a solved problem. Recently, researchers have turned their attention to the problem of multimodal affect sensing in the hope that combining different information sources would provide great improvements. However, reported results fall short of the expectations, indicating only modest benefits and occasionally even degradation in performance. We develop temporal Bayesian fusion for continuous real-value estimation of valence, arousal, power, and expectancy dimensions of affect by combining video, audio, and lexical modalities. Our approach provides substantial gains in recognition performance compared to previous work. This is achieved by the use of a powerful temporal prediction model as prior in Bayesian fusion as well as by incorporating uncertainties about the unimodal predictions. The temporal prediction model makes use of time correlations on the affect sequences and employs estimated temporal biases to control the affect estimations at the beginning of conversations. In contrast to other recent methods for combination of modalities our model is simpler, since it does not model relationships between modalities and involves only a few interpretable parameters to be estimated from the training data.

Acoustic and Lexical Representations for Affect Prediction in Spontaneous Conversations.

In this article we investigate what representations of acoustics and word usage are most suitable for predicting dimensions of affect|AROUSAL, VALANCE, POWER and EXPECTANCY|in spontaneous interactions. Our experiments are based on the AVEC 2012 challenge dataset. For lexical representations, we compare corpus-independent features based on psychological word norms of emotional dimensions, as well as corpus-dependent representations. We find that corpus-dependent bag of words approach with mutual information between word and emotion dimensions is by far the best representation. For the analysis of acoustics, we zero in on the question of granularity. We confirm on our corpus that utterance-level features are more predictive than word-level features. Further, we study more detailed representations in which the utterance is divided into regions of interest (ROI), each with separate representation. We introduce two ROI representations, which significantly outperform less informed approaches. In addition we show that acoustic models of emotion can be improved considerably by taking into account annotator agreement and training the model on smaller but reliable dataset. Finally we discuss the potential for improving prediction by combining the lexical and acoustic modalities. Simple fusion methods do not lead to consistent improvements over lexical classifiers alone but improve over acoustic models.

Speaker-sensitive emotion recognition via ranking: Studies on acted and spontaneous speech☆

We introduce a ranking approach for emotion recognition which naturally incorporates information about the general expressivity of speakers. We demonstrate that our approach leads to substantial gains in accuracy compared to conventional approaches. We train ranking SVMs for individual emotions, treating the data from each speaker as a separate query, and combine the predictions from all rankers to perform multi-class prediction. The ranking method provides two natural benefits. It captures speaker specific information even in speaker-independent training/testing conditions. It also incorporates the intuition that each utterance can express a mix of possible emotion and that considering the degree to which each emotion is expressed can be productively exploited to identify the dominant emotion. We compare the performance of the rankers and their combination to standard SVM classification approaches on two publicly available datasets of acted emotional speech, Berlin and LDC, as well as on spontaneous emotional data from the FAU Aibo dataset. On acted data, ranking approaches exhibit significantly better performance compared to SVM classification both in distinguishing a specific emotion from all others and in multi-class prediction. On the spontaneous data, which contains mostly neutral utterances with a relatively small portion of less intense emotional utterances, ranking-based classifiers again achieve much higher precision in identifying emotional utterances than conventional SVM classifiers. In addition, we discuss the complementarity of conventional SVM and ranking-based classifiers. On all three datasets we find dramatically higher accuracy for the test items on whose prediction the two methods agree compared to the accuracy of individual methods. Furthermore on the spontaneous data the ranking and standard classification are complementary and we obtain marked improvement when we combine the two classifiers by late-stage fusion.

CREMA-D: Crowd-sourced Emotional Multimodal Actors Dataset.

People convey their emotional state in their face and voice. We present an audio-visual data set uniquely suited for the study of multi-modal emotion expression and perception. The data set consists of facial and vocal emotional expressions in sentences spoken in a range of basic emotional states (happy, sad, anger, fear, disgust, and neutral). 7,442 clips of 91 actors with diverse ethnic backgrounds were rated by multiple raters in three modalities: audio, visual, and audio-visual. Categorical emotion labels and real-value intensity values for the perceived emotion were collected using crowd-sourcing from 2,443 raters. The human recognition of intended emotion for the audio-only, visual-only, and audio-visual data are 40.9%, 58.2% and 63.6% respectively. Recognition rates are highest for neutral, followed by happy, anger, disgust, fear, and sad. Average intensity levels of emotion are rated highest for visual-only perception. The accurate recognition of disgust and fear requires simultaneous audio-visual cues, while anger and happiness can be well recognized based on evidence from a single modality. The large dataset we introduce can be used to probe other questions concerning the audio-visual perception of emotion.

Prosodic cues for emotion: analysis with discrete characterization of intonation.

In this paper we study the relationship between acted perceptually unambiguous emotion and prosody. Unlike most contemporary approaches which base the analysis of emotion in voice solely on continuous features extracted automatically from the acoustic signal, we analyze the predictive power of discrete characterizations of intonations in the ToBI framework. The goal of our work is to test if particular discrete prosodic events provide significant discriminative power for emotion recognition. Our experiments provide strong evidence that patterns in breaks, boundary tones and type of pitch accent are highly informative of the emotional content of speech. We also present results from automatic prediction of emotion based on ToBI-derived features and compare their prediction power with state-of-the-art bag-of-frame acoustic features. Our results indicate their similar performance in the sentence-dependent emotion prediction tasks, while acoustic features are more robust for the sentence-independent tasks. Finally, we combine ToBI features and acoustic features together and further achieve modest improvements in sentence-independent emotion prediction, particularly in differentiating fear and neutral from other emotion.

Action Unit Models of Facial Expression of Emotion in the Presence of Speech.

Automatic recognition of emotion using facial expressions in the presence of speech poses a unique challenge because talking reveals clues for the affective state of the speaker but distorts the canonical expression of emotion on the face. We introduce a corpus of acted emotion expression where speech is either present (talking) or absent (silent). The corpus is uniquely suited for analysis of the interplay between the two conditions. We use a multimodal decision level fusion classifier to combine models of emotion from talking and silent faces as well as from audio to recognize five basic emotions: anger, disgust, fear, happy and sad. Our results strongly indicate that emotion prediction in the presence of speech from action unit facial features is less accurate when the person is talking. Modeling talking and silent expressions separately and fusing the two models greatly improves accuracy of prediction in the talking setting. The advantages are most pronounced when silent and talking face models are fused with predictions from audio features. In this multi-modal prediction both the combination of modalities and the separate models of talking and silent facial expression of emotion contribute to the improvement.

Combining Video, Audio and Lexical Indicators of Affect in Spontaneous Conversation via Particle Filtering.

We present experiments on fusing facial video, audio and lexical indicators for affect estimation during dyadic conversations. We use temporal statistics of texture descriptors extracted from facial video, a combination of various acoustic features, and lexical features to create regression based affect estimators for each modality. The single modality regressors are then combined using particle filtering, by treating these independent regression outputs as measurements of the affect states in a Bayesian filtering framework, where previous observations provide prediction about the current state by means of learned affect dynamics. Tested on the Audio-visual Emotion Recognition Challenge dataset, our single modality estimators achieve substantially higher scores than the official baseline method for every dimension of affect. Our filtering-based multi-modality fusion achieves correlation performance of 0.344 (baseline: 0.136) and 0.280 (baseline: 0.096) for the fully continuous and word level sub challenges, respectively.