Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech

We describe a statistical approach for modeling dialogue acts in conversational speech, i.e., speech-act-like units such as Statement, Question, Backchannel, Agreement, Disagreement, and Apology. Our model detects and predicts dialogue acts based on lexical, collocational, and prosodic cues, as well as on the discourse coherence of the dialogue act sequence. The dialogue model is based on treating the discourse structure of a conversation as a hidden Markov model and the individual dialogue acts as observations emanating from the model states. Constraints on the likely sequence of dialogue acts are modeled via a dialogue act n-gram. The statistical dialogue grammar is combined with word n-grams, decision trees, and neural networks modeling the idiosyncratic lexical and prosodic manifestations of each dialogue act. We develop a probabilistic integration of speech recognition with dialogue modeling, to improve both speech recognition and dialogue act classification accuracy. Models are trained and evaluated using a large hand-labeled database of 1,155 conversations from the Switchboard corpus of spontaneous human-to-human telephone speech. We achieved good dialogue act labeling accuracy (65% based on errorful, automatically recognized words and prosody, and 71% based on word transcripts, compared to a chance baseline accuracy of 35% and human accuracy of 84%) and a small reduction in word recognition error.Comment: 35 pages, 5 figures. Changes in copy editing (note title spelling changed

Integrating Prosodic and Lexical Cues for Automatic Topic Segmentation

We present a probabilistic model that uses both prosodic and lexical cues for the automatic segmentation of speech into topically coherent units. We propose two methods for combining lexical and prosodic information using hidden Markov models and decision trees. Lexical information is obtained from a speech recognizer, and prosodic features are extracted automatically from speech waveforms. We evaluate our approach on the Broadcast News corpus, using the DARPA-TDT evaluation metrics. Results show that the prosodic model alone is competitive with word-based segmentation methods. Furthermore, we achieve a significant reduction in error by combining the prosodic and word-based knowledge sources.Comment: 27 pages, 8 figure

Word Importance Modeling to Enhance Captions Generated by Automatic Speech Recognition for Deaf and Hard of Hearing Users

People who are deaf or hard-of-hearing (DHH) benefit from sign-language interpreting or live-captioning (with a human transcriptionist), to access spoken information. However, such services are not legally required, affordable, nor available in many settings, e.g., impromptu small-group meetings in the workplace or online video content that has not been professionally captioned. As Automatic Speech Recognition (ASR) systems improve in accuracy and speed, it is natural to investigate the use of these systems to assist DHH users in a variety of tasks. But, ASR systems are still not perfect, especially in realistic conversational settings, leading to the issue of trust and acceptance of these systems from the DHH community. To overcome these challenges, our work focuses on: (1) building metrics for accurately evaluating the quality of automatic captioning systems, and (2) designing interventions for improving the usability of captions for DHH users. The first part of this dissertation describes our research on methods for identifying words that are important for understanding the meaning of a conversational turn within transcripts of spoken dialogue. Such knowledge about the relative importance of words in spoken messages can be used in evaluating ASR systems (in part 2 of this dissertation) or creating new applications for DHH users of captioned video (in part 3 of this dissertation). We found that models which consider both the acoustic properties of spoken words as well as text-based features (e.g., pre-trained word embeddings) are more effective at predicting the semantic importance of a word than models that utilize only one of these types of features. The second part of this dissertation describes studies to understand DHH users\u27 perception of the quality of ASR-generated captions; the goal of this work was to validate the design of automatic metrics for evaluating captions in real-time applications for these users. Such a metric could facilitate comparison of various ASR systems, for determining the suitability of specific ASR systems for supporting communication for DHH users. We designed experimental studies to elicit feedback on the quality of captions from DHH users, and we developed and evaluated automatic metrics for predicting the usability of automatically generated captions for these users. We found that metrics that consider the importance of each word in a text are more effective at predicting the usability of imperfect text captions than the traditional Word Error Rate (WER) metric. The final part of this dissertation describes research on importance-based highlighting of words in captions, as a way to enhance the usability of captions for DHH users. Similar to highlighting in static texts (e.g., textbooks or electronic documents), highlighting in captions involves changing the appearance of some texts in caption to enable readers to attend to the most important bits of information quickly. Despite the known benefits of highlighting in static texts, research on the usefulness of highlighting in captions for DHH users is largely unexplored. For this reason, we conducted experimental studies with DHH participants to understand the benefits of importance-based highlighting in captions, and their preference on different design configurations for highlighting in captions. We found that DHH users subjectively preferred highlighting in captions, and they reported higher readability and understandability scores and lower task-load scores when viewing videos with captions containing highlighting compared to the videos without highlighting. Further, in partial contrast to recommendations in prior research on highlighting in static texts (which had not been based on experimental studies with DHH users), we found that DHH participants preferred boldface, word-level, non-repeating highlighting in captions

A cross-linguistic analysis of the temporal dynamics of turn-taking cues using machine learning as a descriptive tool

In dialogue, speakers produce and perceive acoustic/prosodic turn-taking cues, which are fundamental for negotiating turn exchanges with their interlocutors. However, little of the temporal dynamics and cross-linguistic validity of these cues is known. In this work, we explore a set of acoustic/prosodic cues preceding three turn-transition types (hold, switch and backchannel) in three different languages (Slovak, American English and Argentine Spanish). For this, we use and refine a set of machine learning techniques that enable a finer-grained temporal analysis of such cues, as well as a comparison of their relative explanatory power. Our results suggest that the three languages, despite belonging to distinct linguistic families, share the general usage of a handful of acoustic/prosodic features to signal turn transitions. We conclude that exploiting features such as speech rate, final-word lengthening, the pitch track over the final 200 ms, the intensity track over the final 1000 ms, and noise-to-harmonics ratio (a voice-quality feature) might prove useful for further improving the accuracy of the turn-taking modules found in modern spoken dialogue systems.Fil: Brusco, Pablo. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Vidal, Jazmín. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Beňuš, Štefan. University in Nitra; Eslovaquia. Slovak Academy of Sciences; EslovaquiaFil: Gravano, Agustin. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentin

Recognizing Uncertainty in Speech

We address the problem of inferring a speaker's level of certainty based on prosodic information in the speech signal, which has application in speech-based dialogue systems. We show that using phrase-level prosodic features centered around the phrases causing uncertainty, in addition to utterance-level prosodic features, improves our model's level of certainty classification. In addition, our models can be used to predict which phrase a person is uncertain about. These results rely on a novel method for eliciting utterances of varying levels of certainty that allows us to compare the utility of contextually-based feature sets. We elicit level of certainty ratings from both the speakers themselves and a panel of listeners, finding that there is often a mismatch between speakers' internal states and their perceived states, and highlighting the importance of this distinction.Comment: 11 page

Fillers in Spoken Language Understanding: Computational and Psycholinguistic Perspectives

Disfluencies (i.e. interruptions in the regular flow of speech), are ubiquitous to spoken discourse. Fillers ("uh", "um") are disfluencies that occur the most frequently compared to other kinds of disfluencies. Yet, to the best of our knowledge, there isn't a resource that brings together the research perspectives influencing Spoken Language Understanding (SLU) on these speech events. This aim of this article is to synthesise a breadth of perspectives in a holistic way; i.e. from considering underlying (psycho)linguistic theory, to their annotation and consideration in Automatic Speech Recognition (ASR) and SLU systems, to lastly, their study from a generation standpoint. This article aims to present the perspectives in an approachable way to the SLU and Conversational AI community, and discuss moving forward, what we believe are the trends and challenges in each area.Comment: To appear in TAL Journa

Detecting Levels of Interest from Spoken Dialog with Multistream Prediction Feedback and Similarity Based Hierarchical Fusion Learning

Detecting levels of interest from speakers is a new problem in Spoken Dialog Understanding with significant impact on real world business applications. Previous work has focused on the analysis of traditional acoustic signals and shallow lexical features. In this paper, we present a novel hierarchical fusion learning model that takes feedback from previous multistream predictions of prominent seed samples into account and uses a mean cosine similarity measure to learn rules that improve reclassification. Our method is domain-independent and can be adapted to other speech and language processing areas where domain adaptation is expensive to perform. Incorporating Discriminative Term Frequency and Inverse Document Frequency (DTFIDF), lexical affect scoring, and low and high level prosodic and acoustic features, our experiments outperform the published results of all systems participating in the 2010 Interspeech Paralinguistic Affect Subchallenge

Cross-domain analysis of discourse markers in European Portuguese

This paper presents an analysis of discourse markers in two spontaneous speech corpora for European Portuguese - university lectures and map-task dialogues - and also in a collection of tweets, aiming at contributing to their categorization, scarcely existent for European Portuguese. Our results show that the selection of discourse markers is domain and speaker dependent. We also found that the most frequent discourse markers are similar in all three corpora, despite tweets containing discourse markers not found in the other two corpora. In this multidisciplinary study, comprising both a linguistic perspective and a computational approach, discourse markers are also automatically discriminated from other structural metadata events, namely sentence-like units and disfluencies. Our results show that discourse markers and disfluencies tend to co-occur in the dialogue corpus, but have a complementary distribution in the university lectures. We used three acoustic-prosodic feature sets and machine learning to automatically distinguish between discourse markers, disfluencies and sentence-like units. Our in-domain experiments achieved an accuracy of about 87% in university lectures and 84% in dialogues, in line with our previous results. The eGeMAPS features, commonly used for other paralinguistic tasks, achieved a considerable performance on our data, especially considering the small size of the feature set. Our results suggest that turn-initial discourse markers are usually easier to classify than disfluencies, a result also previously reported in the literature. We conducted a cross-domain evaluation in order to evaluate the robustness of the models across domains. The results achieved are about 11%-12% lower, but we conclude that data from one domain can still be used to classify the same events in the other. Overall, despite the complexity of this task, these are very encouraging state-of-the-art results. Ultimately, using exclusively acoustic-prosodic cues, discourse markers can be fairly discriminated from disfluencies and SUs. In order to better understand the contribution of each feature, we have also reported the impact of the features in both the dialogues and the university lectures. Pitch features are the most relevant ones for the distinction between discourse markers and disfluencies, namely pitch slopes. These features are in line with the wide pitch range of discourse markers, in a continuum from a very compressed pitch range to a very wide one, expressed by total deaccented material or H+L* L* contours, with upstep H tones