Computational modeling of turn-taking dynamics in spoken conversations

The study of human interaction dynamics has been at the center for multiple research disciplines in- cluding computer and social sciences, conversational analysis and psychology, for over decades. Recent interest has been shown with the aim of designing computational models to improve human-machine interaction system as well as support humans in their decision-making process. Turn-taking is one of the key aspects of conversational dynamics in dyadic conversations and is an integral part of human- human, and human-machine interaction systems. It is used for discourse organization of a conversation by means of explicit phrasing, intonation, and pausing, and it involves intricate timing. In verbal (e.g., telephone) conversation, the turn transitions are facilitated by inter- and intra- speaker silences and over- laps. In early research of turn-taking in the speech community, the studies include durational aspects of turns, cues for turn yielding intention and lastly designing turn transition modeling for spoken dia- log agents. Compared to the studies of turn transitions very few works have been done for classifying overlap discourse, especially the competitive act of overlaps and function of silences. Given the limitations of the current state-of-the-art, this dissertation focuses on two aspects of con- versational dynamics: 1) design automated computational models for analyzing turn-taking behavior in a dyadic conversation, 2) predict the outcome of the conversations, i.e., observed user satisfaction, using turn-taking descriptors, and later these two aspects are used to design a conversational profile for each speaker using turn-taking behavior and the outcome of the conversations. The analysis, experiments, and evaluation has been done on a large dataset of Italian call-center spoken conversations where customers and agents are engaged in real problem-solving tasks. Towards solving our research goal, the challenges include automatically segmenting and aligning speakers’ channel from the speech signal, identifying and labeling the turn-types and its functional aspects. The task becomes more challenging due to the presence of overlapping speech. To model turn- taking behavior, the intension behind these overlapping turns needed to be considered. However, among all, the most critical question is how to model observed user satisfaction in a dyadic conversation and what properties of turn-taking behavior can be used to represent and predict the outcome. Thus, the computational models for analyzing turn-taking dynamics, in this dissertation includes au- tomatic segmenting and labeling turn types, categorization of competitive vs non-competitive overlaps, silences (e.g., lapse, pauses) and functions of turns in terms of dialog acts. The novel contributions of the work presented here are to 1. design of a fully automated turn segmentation and labeling (e.g., agent vs customer’s turn, lapse within the speaker, and overlap) system. 2. the design of annotation guidelines for segmenting and annotating the speech overlaps with the competitive and non-competitive labels. 3. demonstrate how different channels of information such as acoustic, linguistic, and psycholin- guistic feature sets perform in the classification of competitive vs non-competitive overlaps. 4. study the role of speakers and context (i.e., agents’ and customers’ speech) for conveying the information of competitiveness for each individual feature set and their combinations. 5. investigate the function of long silences towards the information flow in a dyadic conversation. The extracted turn-taking cues is then used to automatically predict the outcome of the conversation, which is modeled from continuous manifestations of emotion. The contributions include 1. modeling the state of the observed user satisfaction in terms of the final emotional manifestation of the customer (i.e., user). 2. analysis and modeling turn-taking properties to display how each turn type influence the user satisfaction. 3. study of how turn-taking behavior changes within each emotional state. Based on the studies conducted in this work, it is demonstrated that turn-taking behavior, specially competitiveness of overlaps, is more than just an organizational tool in daily human interactions. It represents the beneficial information and contains the power to predict the outcome of the conversation in terms of satisfaction vs not-satisfaction. Combining the turn-taking behavior and the outcome of the conversation, the final and resultant goal is to design a conversational profile for each speaker. Such profiled information not only facilitate domain experts but also would be useful to the call center agent in real time. These systems are fully automated and no human intervention is required. The findings are po- tentially relevant to the research of overlapping speech and automatic analysis of human-human and human-machine interactions

Acoustic Features of Different Types of Laughter in North Sami Conversational Speech

Peer reviewe

Spoken content retrieval: A survey of techniques and technologies

Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR

Interviewer speech and the success of survey invitations

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94771/1/rssa1064.pd

Towards Better Understanding of Spoken Conversations: Assessment of Emotion and Sentiment

Emotions play a vital role in our daily life as they help us convey information impossible to express verbally to other parties. While humans can easily perceive emotions, these are notoriously difficult to define and recognize by machines. However, automatically detecting the emotion of a spoken conversation can be useful for a diverse range of applications such as human-machine interaction and conversation analysis. In this thesis, we present several approaches based on machine learning to recognize emotion from isolated utterances and long recordings. Isolated utterances are usually shorter than 10s in duration and are assumed to contain only one major emotion. One of the main obstacles in achieving high emotion recognition accuracy is the lack of large annotated data. We propose to mitigate this problem by using transfer learning and data augmentation techniques. We show that x-vector representations extracted from speaker recognition models (x-vector models) contain emotion predictive information and adapting those models provide significant improvements in emotion recognition performance. To further improve the performance, we propose a novel perceptually motivated data augmentation method, Copy-Paste on isolated utterances. This method is based on the assumption that the presence of emotions other than neutral dictates a speaker ’s overall perceived emotion in a recording. As isolated utterances are assumed to contain only one emotion, the proposed models make predictions on the utterance level. However, these models can not be directly applied to conversations that can have multiple emotions unless we know the locations of emotion boundaries. In this work, we propose to recognize emotions in the conversations by doing frame-level classification where predictions are made at regular intervals. We compare models trained on isolated utterances and conversations. We propose a data augmentation method, DiverseCatAugment based on attention operation to improve the transformer models. To further improve the performance, we incorporate the turn-taking structure of the conversations into our models. Annotating utterances with emotions is not a simple task and it depends on the number of emotions used for annotation. However, annotation schemes can be changed to reduce annotation efforts based on application. We consider one such application: predicting customer satisfaction (CSAT) in a call center conversation where the goal is to predict the overall sentiment of the customer. We conduct a comprehensive search for adequate acoustic and lexical representations at different granular levels of conversations. We show that the methods that use transfer learning (x-vectors and CSAT Tracker) perform best. Our error analysis shows that the calls where customers accomplished their goal but were still dissatisfied are the most difficult to predict correctly, and the customer’s speech is more emotional compared to the agent’s speech

Fully generated scripted dialogue for embodied agents

This paper presents the NECA approach to the generation of dialogues between Embodied Conversational Agents (ECAs). This approach consist of the automated construction of an abstract script for an entire dialogue (cast in terms of dialogue acts), which is incrementally enhanced by a series of modules and finally ''performed'' by means of text, speech and body language, by a cast of ECAs. The approach makes it possible to automatically produce a large variety of highly expressive dialogues, some of whose essential properties are under the control of a user. The paper discusses the advantages and disadvantages of NECA's approach to Fully Generated Scripted Dialogue (FGSD), and explains the main techniques used in the two demonstrators that were built. The paper can be read as a survey of issues and techniques in the construction of ECAs, focusing on the generation of behaviour (i.e., focusing on information presentation) rather than on interpretation

Review of Automatic Speech Recognition Methodologies

DTFACT-14-D-00004,692M152240001This report highlights the crucial role of Automatic Speech Recognition (ASR) techniques in enhancing safety for air traffic control (ATC) in terminal environments. ASR techniques facilitate efficient and accurate transcription of verbal communications, reducing the likelihood of errors. The report also details the evolution of ASR technologies, converging to machine learning approaches from Hidden Markov Models (HMMs), Deep Neural Networks (DNNs) to End-to-End models. Finally, the report details the latest advancements in ASR techniques, focusing on transformer-based models that have outperformed traditional ASR approaches and achieved state-of-the-art results on ASR benchmarks