Podcasts are conversational in nature and speaker changes are frequent-requiring speaker diarization for content understanding.We propose an unsupervised technique for speaker diarization without relying on language-specific components.The algorithm is overlap-aware and does not require information about the number of speakers.Our approach shows 79% improvement on purity scores (34% on F-score) against the Google Cloud Platform solution on podcast data.

With the recent trend of applying machine learning in every aspect of human life, it is important to incorporate fairness into the core of the predictive algorithms. We address the problem of predicting the quality of public speeches while being fair with respect to sensitive attributes of the speakers, e.g. gender and race. We use the TED talks as an input repository of public speeches because it consists of speakers from a diverse community and has a wide outreach. Utilizing the theories of Causal Models, Counterfactual Fairness and state-of-the-art neural language models, we propose a mathematical framework for fair prediction of the public speaking quality. We employ grounded assumptions to construct a causal model capturing how different attributes affect public speaking quality. This causal model contributes in generating counterfactual data to train a fair predictive model. Our framework is general enough to utilize any assumption within the causal model. Experimental results show that while prediction accuracy is comparable to recent work on this dataset, our predictions are counterfactually fair with respect to a novel metric when compared to true data labels. The FairyTED setup not only allows organizers to make informed and diverse selection of speakers from the unobserved counterfactual possibilities but it also ensures that viewers and new users are not influenced by unfair and unbalanced ratings from arbitrary visitors to the ted.com website when deciding to view a talk.

Md Kamrul Hasan, Wasifur Rahman, AmirAli Bagher Zadeh, Jianyuan Zhong, Md Iftekhar Tanveer, Louis-Philippe Morency, Mohammed (Ehsan) Hoque. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Telling a great story often involves a deliberate alteration of emotions. In this paper, we objectively measure and analyze the narrative trajectories of stories in public speaking and their impact on subjective ratings. We conduct the analysis using the transcripts of over 2000 TED talks and estimate potential audience response using over 5 million spontaneous annotations from the viewers. We use IBM Watson Tone Analyzer to extract sentence-wise emotion, language, and social scores. Our study indicates that it is possible to predict (with AUC as high as 0.88) the subjective ratings of the audience by analyzing the narrative trajectories. Additionally, we find that some trajectories (for example, a flat trajectory of joy) correlate well with some specific ratings (e.g. "Longwinded') assigned by the viewers. Such an association could be useful in forecasting audience responses using objective analysis.

We present a computational framework for automatically quantifying verbal and nonverbal behaviors in the context of job interviews. The proposed framework is trained by analyzing the videos of 138 interview sessions with 69 internship-seeking undergraduates at the Massachusetts Institute of Technology (MIT). Our automated analysis includes facial expressions (e.g., smiles, head gestures, facial tracking points), language (e.g., word counts, topic modeling), and prosodic information (e.g., pitch, intonation, and pauses) of the interviewees. The ground truth labels are derived by taking a weighted average over the ratings of nine independent judges. Our framework can automatically predict the ratings for interview traits such as excitement, friendliness, and engagement with correlation coefficients of 0.70 or higher, and can quantify the relative importance of prosody, language, and facial expressions. By analyzing the relative feature weights learned by the regression models, our framework recommends to speak more fluently, use fewer filler words, speak as “we” (versus “I”), use more unique words, and smile more. We also find that the students who were rated highly while answering the first interview question were also rated highly overall (i.e., first impression matters). Finally, our MIT Interview dataset is available to other researchers to further validate and expand our findings.

Many individuals exhibit unconscious body movements called mannerisms while speaking. These repeated changes often distract the audience when not relevant to the verbal context. We present an intelligent interface that can automatically extract human gestures using Microsoft Kinect to make speakers aware of their mannerisms. We use a sparsity-based algorithm, Shift Invariant Sparse Coding, to automatically extract the patterns of body movements. These patterns are displayed in an interface with subtle question and answer-based feedback scheme that draws attention to the speaker's body language. Our formal evaluation with 27 participants shows that the users became aware of their body language after using the system. In addition, when independent observers annotated the accuracy of the algorithm for every extracted pattern, we find that the patterns extracted by our algorithm is significantly (p<0.001) more accurate than just random selection. This represents a strong evidence that the algorithm is able to extract human-interpretable body movement patterns. An interactive demo of AutoManner is available at http://tinyurl.com/AutoManner.

A large number of people rate public speaking as their top fear. What if these individuals were given an intelligent interface that provides live feedback on their speaking skills? In this paper, we present Rhema, an intelligent user interface for Google Glass to help people with public speaking. The interface automatically detects the speaker's volume and speaking rate in real time and provides feedback during the actual delivery of speech. While designing the interface, we experimented with two different strategies of information delivery: 1) Continuous streams of information, and 2) Sparse delivery of recommendation. We evaluated our interface with 30 native English speakers. Each participant presented three speeches (avg. duration 3 minutes) with 2 different feedback strategies (continuous, sparse) and a baseline (no feeback) in a random order. The participants were significantly more pleased (p < 0.05) with their speech while using the sparse feedback strategy over the continuous one and no feedback.

We present a framework for unsupervised detection of nonverbal behavioral cues---hand gestures, pose, body movements, etc.---from a collection of motion capture (MoCap) sequences in a public speaking setting. We extract the cues by solving a sparse and shift-invariant dictionary learning problem, known as shift-invariant sparse coding. We find that the extracted behavioral cues are human-interpretable in the context of public speaking. Our technique can be applied to automatically identify the common patterns of body movements and the time-instances of their occurrences, minimizing time and efforts needed for manual detection and coding of nonverbal human behaviors.

In this paper we present a wearable prototype that can automatically recognize affective cues such as number of people present, their age and gender distributions given an image. We customize the prototype in the context of helping people with visual impairments to better navigate social scenarios. Running an experiment to validate this technology in social scenarios remains part of our future work.

The inability to access non-verbal cues is a setback for people who are blind or visually impaired. A visual-to-auditory Sensory Substitution Device (SSD) may help improve the quality of their lives by transforming visual cues into auditory cues. In this paper, we describe the design and development of a robust and real-time SSD called iFEPS -- improved Facial Expression Perception through Sound. The implementation of the iFEPS evolved over time through a participatory design process. We conducted both subjective and objective experiments to quantify the usability of the system. Evaluation with 14 subjects (7 blind + 7 blind-folded) shows that the users were able to perceive the facial expressions in most of the time. In addition, the overall subjective usability of the system was found to be scoring 4.02 in a 5 point Likert scale.

This work demonstrates a visual-to-auditory Sensory Substitution System (SSD) called Facial Expression Perception through Sound (FEPS). It is designed to enable the visually impaired people to participate in a more effective social communication by perceiving their interlocutor's facial expressions. The earlier SSDs provided feedback on inferred emotions, where as, this system responds to the facial movements. This is a better method than emotion inference due to complexities in expression-to-emotion mapping, the problem of capturing multitude of possible emotions derived from a limited facial movements and the difficulty to correctly predict emotions due to lack in ground truth data. In this work, the user's ability to understand the facial expressions has been ensured by a usability study.

The lack of ability to perceive emotions and affective states is a setback for people who are blind or visually impaired in professional and social communications. Towards developing assistive technology solution in facilitating natural dyadic conversations for people with such disability, this paper describes the development of a smart phone based system called interactive mobile affect perception system (iMAPS) for prediction of affective dimensions (valence-arousal-dominance). The proposed solution utilizes an Android platform in conjunction with a wireless network to build a fully integrated iMAPS. Empirical analyses were conducted to measure the efficacy and utility of the proposed solution. It was found that the proposed framework can predict affect dimensions with good accuracy (Maximum Correlation Coefficient for valence: 0.68, arousal: 0.71, and dominance: 0.67) in natural dyadic conversation. The overall minimum and maximum response times are (181 milliseconds) and (500 milliseconds), respectively.