Speech Emotion Recognition Considering Local Dynamic Features

Recently, increasing attention has been directed to the study of the speech emotion recognition, in which global acoustic features of an utterance are mostly used to eliminate the content differences. However, the expression of speech emotion is a dynamic process, which is reflected through dynamic durations, energies, and some other prosodic information when one speaks. In this paper, a novel local dynamic pitch probability distribution feature, which is obtained by drawing the histogram, is proposed to improve the accuracy of speech emotion recognition. Compared with most of the previous works using global features, the proposed method takes advantage of the local dynamic information conveyed by the emotional speech. Several experiments on Berlin Database of Emotional Speech are conducted to verify the effectiveness of the proposed method. The experimental results demonstrate that the local dynamic information obtained with the proposed method is more effective for speech emotion recognition than the traditional global features.Comment: 10 pages, 3 figures, accepted by ISSP 201

Toward media collection-based storytelling

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2007.Includes bibliographical references (p. 113-118).Life is filled with stories. Modern technologies enable us to document and share life events with various kinds of media, such as photos, videos, etc. But people still find it time-consuming to select and arrange media fragments to create coherent and engaging narratives. This thesis proposes a novel storytelling system called Storied Navigation, which lets users assemble a sequence of video clips based on their roles in telling a story, rather than solely by explicit start and end times. Storied Navigation uses textual annotations expressed in unconstrained natural language, using parsing and Commonsense reasoning to deduce possible connections between the narrative intent of the storyteller, and descriptions of events and characters in the video. It helps users increase their familiarity with a documentary video corpus. It helps them develop story threads by prompting them with recommendations of alternatives as well as possible continuations for each selected video clip. We view it as a promising first step towards transforming today's fragmented media production experience into an enjoyable, integrated storytelling activity.Edward Yu-Te Chen.S.M

Automatic Emotion Recognition: Quantifying Dynamics and Structure in Human Behavior.

Emotion is a central part of human interaction, one that has a huge influence on its overall tone and outcome. Today's human-centered interactive technology can greatly benefit from automatic emotion recognition, as the extracted affective information can be used to measure, transmit, and respond to user needs. However, developing such systems is challenging due to the complexity of emotional expressions and their dynamics in terms of the inherent multimodality between audio and visual expressions, as well as the mixed factors of modulation that arise when a person speaks. To overcome these challenges, this thesis presents data-driven approaches that can quantify the underlying dynamics in audio-visual affective behavior. The first set of studies lay the foundation and central motivation of this thesis. We discover that it is crucial to model complex non-linear interactions between audio and visual emotion expressions, and that dynamic emotion patterns can be used in emotion recognition. Next, the understanding of the complex characteristics of emotion from the first set of studies leads us to examine multiple sources of modulation in audio-visual affective behavior. Specifically, we focus on how speech modulates facial displays of emotion. We develop a framework that uses speech signals which alter the temporal dynamics of individual facial regions to temporally segment and classify facial displays of emotion. Finally, we present methods to discover regions of emotionally salient events in a given audio-visual data. We demonstrate that different modalities, such as the upper face, lower face, and speech, express emotion with different timings and time scales, varying for each emotion type. We further extend this idea into another aspect of human behavior: human action events in videos. We show how transition patterns between events can be used for automatically segmenting and classifying action events. Our experimental results on audio-visual datasets show that the proposed systems not only improve performance, but also provide descriptions of how affective behaviors change over time. We conclude this dissertation with the future directions that will innovate three main research topics: machine adaptation for personalized technology, human-human interaction assistant systems, and human-centered multimedia content analysis.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133459/1/yelinkim_1.pd

USING DEEP LEARNING-BASED FRAMEWORK FOR CHILD SPEECH EMOTION RECOGNITION

Biological languages of the body through which human emotion can be detected abound including heart rate, facial expressions, movement of the eyelids and dilation of the eyes, body postures, skin conductance, and even the speech we make. Speech emotion recognition research started some three decades ago, and the popular Interspeech Emotion Challenge has helped to propagate this research area. However, most speech recognition research is focused on adults and there is very little research on child speech. This dissertation is a description of the development and evaluation of a child speech emotion recognition framework. The higher-level components of the framework are designed to sort and separate speech based on the speaker’s age, ensuring that focus is only on speeches made by children. The framework uses Baddeley’s Theory of Working Memory to model a Working Memory Recurrent Network that can process and recognize emotions from speech. Baddeley’s Theory of Working Memory offers one of the best explanations on how the human brain holds and manipulates temporary information which is very crucial in the development of neural networks that learns effectively. Experiments were designed and performed to provide answers to the research questions, evaluate the proposed framework, and benchmark the performance of the framework with other methods. Satisfactory results were obtained from the experiments and in many cases, our framework was able to outperform other popular approaches. This study has implications for various applications of child speech emotion recognition such as child abuse detection and child learning robots

Applied and Computational Linguistics

Розглядається сучасний стан прикладної та комп’ютерної лінгвістики, проаналізовано лінгвістичні теорії 20-го – початку 21-го століть під кутом розмежування різних аспектів мови з метою формалізованого опису у електронних лінгвістичних ресурсах. Запропоновано критичний огляд таких актуальних проблем прикладної (комп’ютерної) лінгвістики як укладання комп’ютерних лексиконів та електронних текстових корпусів, автоматична обробка природної мови, автоматичний синтез та розпізнавання мовлення, машинний переклад, створення інтелектуальних роботів, здатних сприймати інформацію природною мовою. Для студентів та аспірантів гуманітарного профілю, науково-педагогічних працівників вищих навчальних закладів України

Low-Resource Unsupervised NMT:Diagnosing the Problem and Providing a Linguistically Motivated Solution

Unsupervised Machine Translation hasbeen advancing our ability to translatewithout parallel data, but state-of-the-artmethods assume an abundance of mono-lingual data. This paper investigates thescenario where monolingual data is lim-ited as well, finding that current unsuper-vised methods suffer in performance un-der this stricter setting. We find that theperformance loss originates from the poorquality of the pretrained monolingual em-beddings, and we propose using linguis-tic information in the embedding train-ing scheme. To support this, we look attwo linguistic features that may help im-prove alignment quality: dependency in-formation and sub-word information. Us-ing dependency-based embeddings resultsin a complementary word representationwhich offers a boost in performance ofaround 1.5 BLEU points compared to stan-dardWORD2VECwhen monolingual datais limited to 1 million sentences per lan-guage. We also find that the inclusion ofsub-word information is crucial to improv-ing the quality of the embedding