Carlos Busso

Analysis of emotion recognition using facial expressions, speech and multimodal information

The interaction between human beings and computers will be more natural if computers are able to perceive and respond to human non-verbal communication such as emotions. Although several approaches have been proposed to recognize human emotions based on facial expressions or speech, relatively limited work has been done to fuse these two, and other, modalities to improve the accuracy and robustness of the emotion recognition system. This paper analyzes the strengths and the limitations of systems based only on facial expressions or acoustic information. It also discusses two approaches used to fuse these two modalities: decision level and feature level integration. Using a database recorded from an actress, four emotions were classified: sadness, anger, happiness, and neutral state. By the use of markers on her face, detailed facial motions were captured with motion capture, in conjunction with simultaneous speech recordings. The results reveal that the system based on facial expression gave better performance than the system based on just acoustic information for the emotions considered. Results also show the complementarily of the two modalities and that when these two modalities are fused, the performance and the robustness of the emotion recognition system improve measurably.

Emotion recognition using a hierarchical binary decision tree approach

Automated emotion state tracking is a crucial element in the computational study of human communication behaviors. It is important to design robust and reliable emotion recognition systems that are suitable for real-world applications both to enhance analytical abilities to support human decision making and to design human-machine interfaces that facilitate efficient communication. We introduce a hierarchical computational structure to recognize emotions. The proposed structure maps an input speech utterance into one of the multiple emotion classes through subsequent layers of binary classifications. The key idea is that the levels in the tree are designed to solve the easiest classification tasks first, allowing us to mitigate error propagation. We evaluated the classification framework on two different emotional databases using acoustic features, the AIBO database and the USC IEMOCAP database. In the case of the AIBO database, we obtain a balanced recall on each of the individual emotion classes using this hierarchical structure. The performance measure of the average unweighted recall on the evaluation data set improves by 3.37% absolute (8.82% relative) over a Support Vector Machine baseline model. In the USC IEMOCAP database, we obtain an absolute improvement of 7.44% (14.58%) over a baseline Support Vector Machine modeling. The results demonstrate that the presented hierarchical approach is effective for classifying emotional utterances in multiple database contexts.

Analysis of Emotionally Salient Aspects of Fundamental Frequency for Emotion Detection

During expressive speech, the voice is enriched to convey not only the intended semantic message but also the emotional state of the speaker. The pitch contour is one of the important properties of speech that is affected by this emotional modulation. Although pitch features have been commonly used to recognize emotions, it is not clear what aspects of the pitch contour are the most emotionally salient. This paper presents an analysis of the statistics derived from the pitch contour. First, pitch features derived from emotional speech samples are compared with the ones derived from neutral speech, by using symmetric Kullback-Leibler distance. Then, the emotionally discriminative power of the pitch features is quantified by comparing nested logistic regression models. The results indicate that gross pitch contour statistics such as mean, maximum, minimum, and range are more emotionally prominent than features describing the pitch shape. Also, analyzing the pitch statistics at the utterance level is found to be more accurate and robust than analyzing the pitch statistics for shorter speech regions (e.g., voiced segments). Finally, the best features are selected to build a binary emotion detection system for distinguishing between emotional versus neutral speech. A new two-step approach is proposed. In the first step, reference models for the pitch features are trained with neutral speech, and the input features are contrasted with the neutral model. In the second step, a fitness measure is used to assess whether the input speech is similar to, in the case of neutral speech, or different from, in the case of emotional speech, the reference models. The proposed approach is tested with four acted emotional databases spanning different emotional categories, recording settings, speakers and languages. The results show that the recognition accuracy of the system is over 77% just with the pitch features (baseline 50%). When compared to conventional classification schemes, the proposed approach performs better in terms of both accuracy and robustness.

Rigid Head Motion in Expressive Speech Animation: Analysis and Synthesis

Rigid head motion is a gesture that conveys important nonverbal information in human communication, and hence it needs to be appropriately modeled and included in realistic facial animations to effectively mimic human behaviors. In this paper, head motion sequences in expressive facial animations are analyzed in terms of their naturalness and emotional salience in perception. Statistical measures are derived from an audiovisual database, comprising synchronized facial gestures and speech, which revealed characteristic patterns in emotional head motion sequences. Head motion patterns with neutral speech significantly differ from head motion patterns with emotional speech in motion activation, range, and velocity. The results show that head motion provides discriminating information about emotional categories. An approach to synthesize emotional head motion sequences driven by prosodic features is presented, expanding upon our previous framework on head motion synthesis. This method naturally models the specific temporal dynamics of emotional head motion sequences by building hidden Markov models for each emotional category (sadness, happiness, anger, and neutral state). Human raters were asked to assess the naturalness and the emotional content of the facial animations. On average, the synthesized head motion sequences were perceived even more natural than the original head motion sequences. The results also show that head motion modifies the emotional perception of the facial animation especially in the valence and activation domain. These results suggest that appropriate head motion not only significantly improves the naturalness of the animation but can also be used to enhance the emotional content of the animation to effectively engage the users

Interrelation Between Speech and Facial Gestures in Emotional Utterances: A Single Subject Study

The verbal and nonverbal channels of human communication are internally and intricately connected. As a result, gestures and speech present high levels of correlation and coordination. This relationship is greatly affected by the linguistic and emotional content of the message. The present paper investigates the influence of articulation and emotions on the interrelation between facial gestures and speech. The analyses are based on an audio-visual database recorded from an actress with markers attached to her face, who was asked to read semantically neutral sentences, expressing four emotion states (neutral, sadness, happiness, and anger). A multilinear regression framework is used to estimate facial features from acoustic speech parameters. The levels of coupling between the communication channels are quantified by using Pearsons correlation between the recorded and estimated facial features. The results show that facial and acoustic features are strongly interrelated, showing levels of correlation higher than r = 0.8 when the mapping is computed at sentence-level using spectral envelope speech features. The results reveal that the lower face region provides the highest activeness and correlation levels. Furthermore, the correlation levels present significant interemo- tional differences, which suggest that emotional content affect the relationship between facial gestures and speech. Principal component analysis (PCA) shows that the audiovisual mapping parameters are grouped in a smaller subspace, which suggests that there is an emotion-dependent structure that is preserved from across sentences. The results suggest that this internal structure seems to be easy to model when prosodic-features are used to estimate the audiovisual mapping. The results also reveal that the correlation levels within a sentence vary according to broad phonetic properties presented in the sentence. Consonants, especially unvoiced and fricative sounds, present the lowest correlation levels. Likewise, the results show that facial gestures are linked at different resolutions. While the orofacial area is locally connected with the speech, other facial gestures such as eyebrow motion are linked only at the sentence-level. The results presented here have important implications for applications such as facial animation and multimodal emotion recognition.

Natural head motion synthesis driven by acoustic prosodic features

Natural head motion is important to realistic facial animation and engaging human–computer interactions. In this paper, we present a novel data‐driven approach to synthesize appropriate head motion by sampling from trained hidden markov models (HMMs). First, while an actress recited a corpus specifically designed to elicit various emotions, her 3D head motion was captured and further processed to construct a head motion database that included synchronized speech information. Then, an HMM for each discrete head motion representation (derived directly from data using vector quantization) was created by using acoustic prosodic features derived from speech. Finally, first‐order Markov models and interpolation techniques were used to smooth the synthesized sequence. Our comparison experiments and novel synthesis results show that synthesized head motions follow the temporal dynamic behavior of real human subjects. Copyright

Interpreting ambiguous emotional expressions

Emotion expression is a complex process involving dependencies based on time, speaker, context, mood, personality, and culture. Emotion classification algorithms designed for real-world application must be able to interpret the emotional content of an utterance or dialog given the modulations resulting from these and other dependencies. Algorithmic development often rests on the assumption that the input emotions are uniformly recognized by a pool of evaluators. However, this style of consistent prototypical emotion expression often does not exist outside of a laboratory environment. This paper presents methods for interpreting the emotional content of non-prototypical utterances. These methods include modeling across multiple time-scales and modeling interaction dynamics between interlocutors. This paper recommends classifying emotions based on emotional profiles, or soft-labels, of emotion expression rather than relying on just raw acoustic features or categorical hard labels. Emotion expression is both interactive and dynamic. Consequently, to accurately recognize emotional content, these aspects must be incorporated during algorithmic design to improve classification performance.

Smart room: participant and speaker localization and identification

Our long-term objective is to create smart room technologies that are aware of the users presence and their behavior and can become an active, but not an intrusive, part of the interaction. In this work, we present a multimodal approach for estimating and tracking the location and identity of the participants including the active speaker. Our smart room design contains three user-monitoring systems: four CCD cameras, an omnidirectional camera and a 16 channel microphone array. The various sensory modalities are processed both individually and jointly and it is shown that the multimodal approach results in significantly improved performance in spatial localization, identification and speech activity detection of the participants.

Visual emotion recognition using compact facial representations and viseme information

Emotion expression is an essential part of human interaction. Rich emotional information is conveyed through the human face. In this study, we analyze detailed motion-captured facial information of ten speakers of both genders during emotional speech. We derive compact facial representations using methods motivated by Principal Component Analysis and speaker face normalization. Moreover, we model emotional facial movements by conditioning on knowledge of speech-related movements (articulation). We achieve average classification accuracies on the order of 75% for happiness, 50–60% for anger and sadness and 35% for neutrality in speaker independent experiments. We also find that dynamic modeling and the use of viseme information improves recognition accuracy for anger, happiness and sadness, as well as for the overall unweighted performance.

Exploring Cross-Modality Affective Reactions for Audiovisual Emotion Recognition

Psycholinguistic studies on human communication have shown that during human interaction individuals tend to adapt their behaviors mimicking the spoken style, gestures, and expressions of their conversational partners. This synchronization pattern is referred to as entrainment. This study investigates the presence of entrainment at the emotion level in cross-modality settings and its implications on multimodal emotion recognition systems. The analysis explores the relationship between acoustic features of the speaker and facial expressions of the interlocutor during dyadic interactions. The analysis shows that 72 percent of the time the speakers displayed similar emotions, indicating strong mutual influence in their expressive behaviors. We also investigate the cross-modality, cross-speaker dependence, using mutual information framework. The study reveals a strong relation between facial and acoustic features of one subject with the emotional state of the other subject. It also shows strong dependence between heterogeneous modalities across conversational partners. These findings suggest that the expressive behaviors from one dialog partner provide complementary information to recognize the emotional state of the other dialog partner. The analysis motivates classification experiments exploiting cross-modality, cross-speaker information. The study presents emotion recognition experiments using the IEMOCAP and SEMAINE databases. The results demonstrate the benefit of exploiting this emotional entrainment effect, showing statistically significant improvements.