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Undergraduate and master students interested in research projects, please come to the CS Research Fair the first week of the semester and bring your C.V.
Sarcasm Detection in Social Media
Sarcasm is a common non-malicious type of false information in social media content. While sarcasm detection has been a discussed topic in the field, this project focuses on the time-relevant, domain-specific COVID-19 related tweets. We combine the state-of-art BERT models with previously identified sarcastic markers to classify the sarcastic tweets in the collected COVID-19 Twitter dataset. We aim to determine features that make these tweets sarcastic including linguistic and nonlinguistic cues. We are also interested in extending this work into sarcastic speech detection where little systematic work is done. The sarcasm project serves as part of the Semafor project.
Participants: Run Chen, Ziwei (Sara) Gong, Julia Hirschberg, Tuhin Chakrabarty, Smaranda Muresan
Semafor: Identifying false information in social media
Our goal in this project is to detect false information in social media and to identify the intent or purpose behind the falsified information. We are beginning by collecting Twitter data on Covid19 and the U. S. 2020 elections and identifying information from the tweets about tweeters and their networks and the information sources they point to. We are also beginning to study different types of intent behind tweets we identify as false or pointing to false information using emotion detection and sarcasm detection. We divide falsification intent into two major categories: malicious and non-malicious, where non-malicious may be humorous or sarcastic or simply misinformation on the part of the tweeter. Other reasons for spreading misinformation might be to produce an emotional reaction toward an individual or group, persuading people to support an individual or group or set of ideas or to “cover up” an embarrassing or criminal act from being believed as true.
Participants: Lin Ai, Zixiaofan (Brenda) Yang, Run Chen, Gitika Bose, Anika Kathuria, Rishabh Narang, Julia Hirschberg
Detecting Deception in Multiple Modalities Across Cultures
The aim of this research is to increase our scientific understanding of deceptive behavior as it is practiced and perceived within and across cultures. A secondary goal is to develop state-of-the-art techniques to detect deceptive behaviors in spoken language. We have built a new corpus of deceptive and non-deceptive speech, using subjects from American, Mandarin, and Arabic adult native speakers. We have examined many possible cues to deception, including acoustic, prosodic and lexical features, subject-dependent features, and entrainment, and personality differences. We have also compared deception detection techniques in multiple corpora and in multiple modalities including facial features as well as text and speech. We have also developed a LieCatcher game which we are using to compare the performance of our classification models to human performance more broadly using crowd-sourcing (see below on Trusted and Mistrusted Speech).
Participants: Sarah Ita Levitan, Laura Willson, Nishmar Cestero, Guozhen An (CUNY), Angel Maredia, Elizabeth Petitti, Molly Scott, Yogesh Singh, Jessica Xiang, Jixuan (Gilbert) Zhang, Rivka Levitan (CUNY), Michelle Levine (Barnard), Andrew Rosenberg (CUNY), Julia Hirschberg
Multimodal Research on Radicalization
In this project we are studying the influence of radicalizing data in social media in persuading viewers to adopt extremist right-wing or left-wing political beliefs. Previous research has developed many theories of how and why radicalization occurs, but less research has been done to empirically test these theories on a large scale and to answer questions about specific features of group methods that are statistically correlated with success in attracting followers. Our goal is to identify, collect and perform careful statistical analysis of online radicalization videos and develop Machine Learning classifiers to identify and thus help us collect a very large corpus for others to use as well in testing radicalization theories. In addition to identifying aspects of videos which appear to lead to radicalization, we want to explore additional questions, including: What are the characteristics of radical video material? Can we measure the effectiveness of different materials? What are the characteristics of individuals who are engaged with extremist content on YouTube? Do users tend to entrain to (unconsciously imitate) the inciters in terms of the language they use (perhaps about certain groups)? Is this evident in the language they adopt? The symbols they use? Can we track changes over time in the material that appears to lead to specific violent incidents? Can we use these findings to predict future incidents -- as well as those unlikely to result in violence?
Participants: Lin Ai, Yogesh Singh, Sarah Ita Levitan, Julia Hirscberg
Gender Differences in Debates
We are examining acoustic and prosodic as well as lexical cues in a large collection of intercollegiate debate tournaments collected from 2008-2018 with information on the debaters and their scores. Our initial goal is to identify differences in debate behavior and success based on gender differences, using transcripts and acoustic-prosodic information. The corpus was collected by our collaborator, Huyen Nguyen.
Participants: Sarah Ita Levitan, Huyen Nguyen, David Lupea, Julia Hirschberg
Identifying Trusted and Mistrusted Speech
Humans rarely perform better than chance at lie detection. To better understand human perception of deception, we created a game framework, LieCatcher, to collect ratings of perceived deception using a large corpus of deceptive and truthful interviews. We analyzed the acoustic-prosodic and linguistic characteristics of language trusted and mistrusted by raters and compared these to characteristics of actual truthful and deceptive language to understand how perception aligns with reality. With this data we built classifiers to automatically distinguish trusted from mistrusted speech which perform significantly better than humans. We next evaluated whether the strategies raters said they used to discriminate between truthful and deceptive responses were in fact useful. Our results show that, while several prosodic and lexical features were consistently perceived as trustworthy, they were not reliable cues. Also, the strategies that judges reported using in deception detection were not helpful for the task. Currently we are experimenting with using LieCatcher to help train humans in lie detection providing helpful cues when they miss a lie.
Participants: Sarah Ita Levitan, Xi (Leslie) Chen, Rebecca Calinsky, Marko Mandic, Xinyue Tan, Michelle Levine, Julia Hirschberg
Prosodic Assignment for TTS
Accurate prosody prediction from text leads to more natural-sounding TTS. In this work, we employ a new set of features to predict ToBI pitch accent and phrase boundaries from text. We investigate a wide variety of text-based features, including many new syntactic features, several types of word em- beddings, co-reference features, LIWC features, and specificity information. We focus our work on the Boston Radio News Corpus, a ToBI-labeled corpus of relatively clean news broad- casts, but also test our classifiers on Audix, a smaller corpus of read news, and on the Columbia Games Corpus, a corpus of conversational speech, in order to test the applicability of our model in cross-corpus settings. Our results show strong performance on both tasks, as well as some promising results for cross-corpus applications of our models. Currently we are preparing the Switchboard Corpus for additional analysis.
Participants: Rose Sloan, Adaeze Adigwe, Isabella Mandis, Sahana Mohandoss, Syed Sarfaraz Ahtar, Bryan Li, Ritvik Shrivastava, Liliann Ulysse, Mariel Werner, Agustin Gravano, Julia Hirschberg
Gender-balanced Charismatic Speech and Charismatic Politician Speech
Following the earlier work in our lab, we started two new projects on charismatic speech: (1) studying the role of demographic information, especially gender, in producing and perceiving charismatic speech using a gender-balanced dataset, and (2) studying charismatic politician speech on a newly collected large-scaled politician speech dataset. We are interested in how the genre of speech, the speaking style, and the perceiver’s demographic information affects perceived traits of politician speech.
Participants: Zixiaofan (Brenda) Yang, Nishmar Cestero, Tomer Aharoni, Brandon Liang, Riku Tabata, Jessica Yin Huynh, Alina Ying, Julia Hirschberg
Modeling Mental Illness from Reddit Posts
We are currently investigating the problem of automatic detection of psychiatric disorders from the linguistic content of social media posts in Reddit. We have collected a large scale dataset of Reddit posts from users with eight types of mental disorders and a control user group. We have extracted and analyzed the linguistic characteristics of posts and have identified differences between these diagnostic groups. We have built strong classification models based on deep contextualized word representations and have found that they outperform previously applied statistical models with simple linguistic features by large margins.
Participants: Sarah Ita Levitan, Zhengping Jiang, Julia Hirschberg
Detecting Hate Speech Directed at Female Journalists
Most efforts at identifying abusive speech online have relied on public corpora that have been scraped from websites using keyword-based queries or released by site or platform owners for research purposes. These are typically labeled by crowd-sourced annotators -- not the targets of the abuse. While this data supports fast development of machine learning classifiers, the models built on them often fail in the context of real-world harassment and abuse, which contain nuances less easily identified by non-targets. We are developing a mixed-methods approach to creating classifiers for abuse and harassment which will leverage direct engagement with the target group in order to achieve high quality and ecological validity of data sets and labels, and to generate deeper insights into the key tactics of bad actors. We are collecting women journalists' experience on Twitter as an initial community of focus but we have identified several structural mechanisms of abuse that we believe will generalize to other target communities.
Participants: Sarah Ita Levitan, Ishaan Arora, Julia Guo, Susan McGregor, Julia Hirschberg
Cross-lingual Emotion and Sentiment Detection in Speech
We have built models for emotion detection in speech, predicting both valence and arousal continuously and utilizing both waveforms and spectrograms as inputs for corpora such as the SEMAINE and RECOLA databases using models trained on these to predict emotion in Low Resource Languages. We are currently studying cross-lingual sentiment detection in speech.
Participants: Zixiaofan (Brenda) Yang, Sheryl Mathew, Julia Hirschberg
Speaker Entrainment in Dialogue Systems
In conversation, people entrain to their partner by adopting that partner's
word choice, or by adapting aspects of their speaking style, such as speaking rate or pitch
range or intensity. Such synchronization is critical to the success of human-human
While lexical entrainment has been investigated experimentally in a number of studies, other
types of entrainment have received less attention. In this project, we are investigating
entrainment along dimensions such as intonational contour, pitch accent, phrasing rate, pitch
range, intensity, laughter, turn-taking and backchanneling behaviors.
An investigation of these behaviors will support the design of better Spoken Dialogue Systems.
While entrainment has been proposed as an important method for inducing users to adopt the
system's lexical items, to improve recognition accuracy, few studies have examined the
importance of systems entraining to their users, to promote more successful and human-like
Participants: Julia Hirschberg, Marian Trnka, Eduard Kuric, Lukas Martak, Andreas Weise, Sarah Ita Levitan, Ramiro H. Galvez, Florencia Savoretti, Sakhia Darjaa (Slovak Academia of Sciences), Laura Willson, Shirley Xia (Shanghai Jiao), Ani Nenkova (University of Pennsylvania), Agustín Gravano (University of Buenos Aires), Enrique Henestroza, Rivka Levitan, Adele Chase, Laura Willson, Stefan Benus (Constantine the Philosopher University), Jens Edlund (KTH), Mattias Heldner (KTH)
Identifying Hyraxes by Their Song
We have obtained a corpus of hyrax songs from a collaborator at Bar-Ilan University which were identified by the name tag of the hyrax from which the songs were collected. Our goal was to be able to identify different hyraxes by differences in their songs. While we did manage to obtain reasonable results we had some issues with amount of data available and background noise in the recordings which were done in the wild.
Participants: Lin Ai, Shivani Ghatge, Lee Koren (Bar-Ilan U), Julia Hirschberg
Differences in Demographics and Trust in News Sources
We investigated differences in the degree of trust readers expressed toward news articles in a corpus collected by the Knight Foundation which asked raters to provide demographic information on gender, age, education, financial level, and political leaning (liberal vs. conservative. We clustered the raters into multiple groups and identified their allocation of trust in media based on their demographics as well as the news source for the articles rated.
Participants: Sarah Ita Levitan, Eric Bolton, Marko Mandic, Julia Hirschberg
LORELEI: Incident Detection in Low Resource Languages
The goal of this project is to build low-resource language speech processing systems in order to support a rapid and effective response to emerging incidents. We approach this goal from two aspects: (1) Detecting incidents in speech using prosodic characteristics only. We assume when an incident happened, the speech might contain emotions such as fear or stress, which is captured in prosodic features. We built cross-lingual incident detection models for all the 27 languages provided in the LORELEI project. (2) Detecting incidents by spotting keywords related to the incidents. We proposed a linguistically-informed training scheme to obtain acoustic word embeddings that can be easily applied to query-by-example keyword search in languages with minimal resources.
Participants: Zixiaofan (Brenda) Yang, Lin Ai, Julia Hirschberg
Multimodal Humor Prediction
In this project, we proposed a novel approach for generating unsupervised humor labels using time-aligned user comments, and predicting humor using speech, text, and visual information. We collected 341 videos of comedy movies, gameplay videos, and satirical talk show videos from one of the largest Chinese video-sharing website. We generated unsupervised humor labels from laughing comments, and found high agreement between these labels and human annotations. From these unsupervised labels, we built deep learning models using features from multiple modalities, which obtained an F1-score of 0.73 in predicting humor on a manually annotated test set.
Participants: Zixiaofan (Brenda) Yang, Lin Ai, Bingyan Hu, Julia Hirschberg
Detecting Hate Speech Targeting Different Religious Groups
This project detects hate speech in online text, where hate speech is defined as abusive speech targeting specific group characteristics, such as ethnic origin, re- ligion, gender, or sexual orientation. While hate speech against any group may exhibit some common characteristics, we observed that hatred against each different group is typically characterized by the use of a small set of high frequency stereotypical words; however, such words may be used in either a positive or a negative sense, making our task similar to that of words sense disambiguation. To build a classifier to detect hate speech we collected and annotated an anti-semitic hate speech corpus. We also developed a mechanism for detecting some commonly used methods of evading common “dirty word” filters. Our pilot classification experiments attained an accuracy of 94%, precision of 68% and recall at 60%, for an F1 measure of .6375.
Participants: William Warner, Matthew Holtzman, Julia Hirschberg with help from Belle Tseng, Kim Capps-Tanaka, Evgeniy Gabrilovish and Martin Zinkevish at Yahoo!
Clarification in Dialogue
In this project we modeled human responses to speech recognition errors from a corpus of human clarification strategies. We employed machine learning techniques to study 1) the decision to either stop and ask a clarification question or to continue the dialogue without clarification, and 2) the decision to ask a targeted clarification question or a more generic question. Targeted clarification questions focus specifically on the part of an utterance that is misrecognized, in contrast to generic requests to ‘please repeat’ or ‘please rephrase’. Our goal is to generate targeted clarification strategies for handling errors in spoken dialogue systems, when needed. Our experiments showed that linguistic features, in particular the inferred part-of-speech of a misrecognized word are predictive of human clarification decisions. A combina- tion of linguistic features predicts a user’s decision to continue or stop a dialogue with an accuracy of 72.8% over a majority baseline accuracy of 59.1%. The same set of features predict the decision to ask a targeted question with an accuracy of 74.6% compared with the majority baseline of 71.8%.
Participants: Svetlana Stoyanchev, Alex Liu, Eli Pincus, Julia Hirschberg
Text-to-Speech Synthesis for Low-Resource Languages
The rapid improvement of speech technology over the past few years has resulted in its widespread adoption by consumers, especially in mobile spoken dialogue systems such as Apple Siri and Google Voice Search. This progress has led to very natural and intelligible text-to-speech (TTS) synthesis for a small number of languages, including English, French, and Mandarin. These high-resource languages (HRLs) have been studied extensively by speech researchers who have built various language tools and collected and annotated massive amounts of speech data in these languages. However, there are thousands of languages in the world (~6500), many of which are spoken by millions of people, which have not been so fortunate to receive this attention from the speech and natural language processing community. Low-resource languages (LRLs), such as Telugu, Tok Pisin, and Vietnamese, for example, do not enjoy rich computational resources and vast amounts of annotated data. Thus, speakers of these languages are deprived of the benefits of modern speech technology which enable us to communicate across language barriers.
We are working towards developing methods of building intelligible, natural-sounding TTS voices out of limited data. While most commercial TTS voices are built from audio recorded by a professional speaker in a controlled acoustic environment, this data can be very time-consuming and expensive to collect. We are exploring the use of radio broadcast news, speech recorded with mobile phones, and other found data for building TTS voices, investigating data selection and model adaptation techniques for making the most out of noisy data.
Participants: Julia Hirschberg, Erica Cooper, Khai-Zhan Lee, Elshadai Testaye Biru, Yishak Tofik Mohammed, David Tofu, Emily Li, Alison Chang, Yocheved Levitan, Luise Valentin Rygaard, Olivia Lundelius, Xinyue Wang, Mert Ussakli
Code switching (CS) is the practice of switching back and forth between the shared languages
of bilingual or multilingual speakers. CS is particularly prevalent in geographic regions with
linguistic boundaries or where there are large immigrant groups sharing a common first
language different from the mainstream language, as in the USA. Different levels of language
(phonological, morphological, syntactic, semantic and discourse-pragmatic) may be involved in
CS in different language pairs and/or genres. Computational tools trained for a single language
such as automatic speech recognition, information extraction or retrieval, or machine translation
systems quickly break down when the input includes CS. A major barrier to research on CS in
computational linguistics has been the lack of large, accurately annotated corpora of CS data.
We are part of a larger team which aims to collect a large repository of CS data, consistently
annotated across different language pairs at different levels of granularity, from phonology/
morphology to pragmatics and discourse, in Modern Standard Arabic with dialectal Arabic,
Arabic-English, Hindi-English, Spanish-English, and Mandarin-English. At Columbia we are
currently focusing on collecting Mandarin-English CS data in social media and in telephone
Participants: Julia Hirschberg, Victor Soto, Nishmar Cestero, Alison Chang, Mona Diab, Thamar Solorio
DEFT: Anomaly Detection in Speech
This project investigates anomaly in speech by looking at behaviors that break the Gricean maxims of cooperative communication. Specifically, we are looking at hedging behaviors wherein the speaker uses cue words (eg. 'maybe', 'could', 'think', etc) to show a reduced commitment to their utterance. Initial research included constructing an annotation manual to accurately identify and label such behavior in speech. Ongoing work is looking at automatic labeling of hedges with the help of lexical and acoustic features. The end goal is to use the presence of hedging and disfluencies as a metric through which we can identify anomalous regions in dialogue.
Participants: Morgan Ulinski, Anna Prokofieva, Julia Hirschberg, Owen Rambow, Vinod Prabhakaran, Smaranda Muresan, Apoorv Agarwal, Anup Kotalwar, Kathy McKeown, Sara Rosenthal, Weiwei Guo
BOLT investigates interactive error handling for speech translation systems. BOLT is DARPA funded joint project with SRI international, University of Marseille, and University of Washington. In this project, we introduce an error-recovery dialogue manager component into a spoken translation system. A spoken translation system allows speakers of two different languages to communicate verbally through a translation application. An error-recovery dialogue manager detects errors in the recognition of utterances and asks the speaker a clarification question before translating the potentially erroneous utterance. Most modern dialogue systems employ generic clarification strategies for recovering from recognition errors by asking a user to repeat or rephrase their previous utterance or asking a yes/no confirmation question. Such generic requests are not natural and tend to frustrate the user. In BOLT, we evaluate the feasibility of using targeted clarification questions that focus specifically on the part of an utterance that contains a predicted recognition error. For example, if a speaker says "Pass me some XXX", where XXX is a misunderstood concept, a system may ask the targeted clarification question "What shall I pass?" instead of a generic request for a repetition. Our approach is based on human strategies for such clarifications. We have collected and analysed a corpus of human responses to misunderstandings in dialogue (Stoyanchev et al., Interdisciplinary Workshop on Feedback Behaviors in Dialog 2012). In order to create targeted clarifications, it is important to detect the error location in the utterance. We used a combination of ASR confidence, lexical, and prosodic features to help identify which words in a spoken sentence are misrecognized (Stoyanchev et al., SLT 2012). Although BOLT evaluates a targeted clarification approach with a speech-to-speech translation application, this approach will also benefit spoken dialogue systems, especially AI systems that accept spoken input with a wide range of concepts and topics.
Participants: Svetlana Stoyanchev, Rose Sloan (Yale University), Mei-Vern Then, Alex Liu, Sunil Khanal, Eli Pincus, Ananta Padney (Barnard College), Jingbo Yaung, Philipp Salletmayer (Graz University)
Text-to-Scene for Field Linguistics
This research aims at creating a novel tool for fieldwork, which we call the WordsEye Linguistics Tool, or WELT. WELT is based on WordsEye, an existing text-to-scene tool which has been developed by in the lab. WordsEye allows for the automatic generation of 3D scenes from written input. The WELT tool will have two modes of operation. In the first mode, English input will automatically generate a picture which can be used to elicit a targeted description in the language being studied. In the second mode, linguists will use an intuitive interface to develop a formal grammar of spatial expressions for the language they are researching. The tool will automatically incorporate this grammar into the existing WordsEye infrastructure to create a text-to-scene system for the new language. Linguists can use this system to verify their grammar with native speakers, easily making changes to it in realtime.
While we intend that the tool will be generally useful, we are initially developing WELT based on scenarios involving Arrernte, an Australian aboriginal language.
Participants: Morgan Ulinski, Bob Coyne, Julia Hirschberg, Owen Rambow, Alexandra Orth, Inna Fetissova (Northeastern University), Myfany Turpin (University of Queensland), Daniel Kaufman (Endangered Language Alliance), Mark Dras (Macquarie University)
The BABEL program aims to develop spoken keyword search systems for diverse low-resource languages. Our group focuses on the use of prosodic features for improving recognition accuracy and keyword search performance, as well as experiments in cross-lingual adaptation of models for identifying prosodic events.
Participants: Victor Soto, Erica Cooper, Andrew Rosenberg, Gideon Mendels, Julia Hirschberg
AuToBI is a tool for the automatic analysis of Standard American English
prosody. Open source and written in Java, AuToBI hypothesizes pitch accents
and phrase boundaries consistent with the ToBI prosodic annotation standard.
The toolkit incluides an acoustic feature extraction frontend, and a
classification backend supported by the
Weka machine learning toolkit.
Participants: Julia Hirschberg, Andrew Rosenberg
Deception in Speech
This project consisted in examining the feasibility of automatic
detection of deception in speech, using linguistic, prosodic, and
other acoustic cues. We were particularly interested in how
individual differences affect the behavior of deceivers, and how
such differences affect the ability of individuals to detect
Our study produced the first cleanly recorded, labeled corpus of
deceptive speech, the Columbia-SRI-Colorado (CSC) Corpus. Our elicitation
paradigm created a context in which the subject was positively
motivated to deceive an interviewer (in contrast to studies in which
subjects are placed in situations where they are led to lie about
potentially guilt inducing behavior). We investigated deception on two
levels: we considered the speaker's overall intention to deceive (or
not) with respect to particular topics, and we examined individual
utterances in terms of their factual content.
Our published work produced a classification system that performs
substantially better than human judges at classifying deceptive and
non-deceptive utterances; a study of the use of filled pauses in
deceptive speech; a method of combining classifiers using different
feature sets; and a perception study showing that the personality
of a listener affects his or her ability to distinguish deceptive
from non-deceptive speech.
Participants: Julia Hirschberg, Frank Enos, Stefan Benus, Jennifer Venditti-Ramprashad, Sarah Friedman, Sarah Gilman, Jared Kennedy, Max Shevyakov, Wayne Thorsen, Alan Yeung, and collaborators from SRI/ICSI and from the University of Colorado at Boulder.
Emotion in Speech
The crux of this research involved characterizing
acoustic and prosodic cues to human emotion, evaluating subjective
judgments of human emotion, as well as exploring when and why certain
emotions become confusable. We conducted on-line surveys designed to
collect subjective judgments of both emotional speech as well as emotional
faces. We observed that machine learning techniques applied to the
prediction of human emotion given acoustic and prosodic information of the
sound tokens yields a prediction rate of 75%-80%.
We also found that our subjects systematically differed on how
they perceived emotion in terms of valency (positive or negative
affect). Furthermore, automatic emotion classification increases if
we model these two groups independently of one another.
Participants: Julia Hirschberg, Jennifer Venditti-Ramprashad, Jackson Liscombe, Sarah Gilman, Daniel Vassilev, Agustín Gravano.
Detecting and Responding to Emotion in Intelligent Tutorial Systems
A tutor uses cues from the student to determine whether information
has been successfully learned or not. These cues may be explicit or
implicit. The first goal of this study is to examine cues to student
emotions − such as frustration and uncertainty − in the context of
speech-enabled intelligent tutorial systems. Such cues include
lexical, prosodic, quality of voice, and contextual information. The
second goal of this study is to evaluate the most appropriate
strategies for responding to (negative) emotional states once they are
detected. The ultimate goal is to increase the enjoyment and learning
of Intelligent Tutoring Systems users.
Participants: Julia Hirschberg, Jennifer Venditti-Ramprashad, Jackson Liscombe, Jeansun Lee (Columbia University); Diane Litman, Katherine Forbes, Scott Silliman (University of Pittsburgh).
Identifying Acoustic, Prosodic, and Phonetic Cues to Individual Variation in Spoken Language
A fundamental challenge for current research on speech science and technology is
understanding individual variation in spoken language. Individuals have their own
speaking styles, depending on many factors, including the dialect and socioeconomic
background of the speaker, as well as contextual variables such as the degree of
familiarity between the speaker and hearer and the register of the speaking
situation, from very casual to very formal (Eskenazi 1992). Even within the same
dialect or register individual variation may occur; for example, in spontaneous
speech, some speakers tend to exhibit more articulation reduction (e.g., reducing or
deletion of function words) than others. In this project, we are working on identifying the acoustic-prosodic and phonetic cues that might contribute to clustering speakers based on their speaking style.
Participants: Fadi Biadsy, Julia Hirschberg, William (Yang) Wang
Extracting Paraphrase Rules from FrameNet and WordNet
FrameNet organizes lexical units into semantic frames with associated
frame elements which represent the core roles of that frame. Each
frame also contains annotated sentences mapping grammatical function
to frame element role for the sample sentences. In our research we've
extracted patterns from these annotated sentences to form paraphrase
rules that cover conversives (e.g. "buy" <-> "sell") as well as other
meaning-preserving verb transformations and alternations such as
"The rats swarmed around the room" <-> "The room was teeming with rats.".
Participants: Bob Coyne, Owen Rambow
WordsEye: Automatic Text-to-Scene Conversion
We live in a vast sea of ever-changing text with few tools available
to help us visualize its meaning. The goal of this research is to
bridge the gap between graphics and language by developing new
theoretical models and supporting technology to create a system that
automatically converts descriptive text into rendered 3D scenes
representing the meaning of that text. This builds upon previous work
done with Richard Sproat in the WordsEye text-to-scene system
(available online at www.wordseye.com). New research directions
include the lexical semantics and knowledge acquisition needed to
semi-automatically construct a new scenario-based lexical
resource. This resource will be used in decoding and making explicit
the oblique contextual elements common in descriptive language for the
purposes of graphical depiction.
Participants: Bob Coyne, Owen Rambow, Julia Hirschberg, Gino Micelli, Cecilia Schudel, Daniel Bauer, Morgan Ulinski, Richard Sproat (OHSU), Masoud Rouhizadeh (OHSU), Yilei Yang, Sam Wiseman, Jack Crawford, Kenny Harvey, Mi Zhou, Yen-Han Lin, Margit Bowler (Reed College), Victor Soto.
People at an instinctual level are drawn to certain public speakers.
What about it makes their speech charismatic? Our research is looking at
acoustic and lexical features from public addresses to locate the source
of the charisma. Though the work so far has been in American English,
parallel work in Arabic may shed light on potential cultural biases in
the perception of charisma.
Participants: Julia Hirschberg, Wisam Dakka, Andrew Rosenberg, Fadi Biadsy, Aron Wahl, Judd Sheinholtz, Sveltlana Stenchikova.
Speech Summarization consists of summarizing spoken data - broadcast news,
telephone conversation, meetings, lectures. We are mainly focusing on
summarization of broadcast news. Our speech summarization research mainly
consists of three different aspects, which are i) Summarization ii)
Information Extraction iii) User Interface. Summarization aspect consists
of extracting significant segments of speech and concatenating them to
provide a coherent summary of the given story in broadcast news.
Information Extraction aspect consists of extracting named entities,
headlines, interviews, different types of speakers. The last part consists
of developing an user-interface that allows us to combine summary of
broadcast news and other extracted information in a coherent and
user-friendly speech browser.
Participants: Julia Hirschberg, Sameer Maskey, Michel Galley, Martin Jansche, Jeansun Lee, Irina Likhtina, Aaron Roth, Lauren Wilcox.
Prosody of Turn-Taking in Dialogue
In conversation there are implicit
rules specifying whose turn it is to talk, and
conventions for switching the turn from one speaker to the other.
For example, interrupting the interlocutor is a (not necessarily
rude) way of grabbing the turn, while formulating a question is
a way of yielding it.
These rules allow dialogues to develop in a coordinated manner.
The goal of this project is to study and characterize those rules and
conventions, in the Columbia Games Corpus
and other corpora.
Participants: Julia Hirschberg; Stefan Benus (Constantine The Philosopher University); Agustín Gravano (University of Buenos Aires), Héctor Chávez, Michael Mulley, Enrique Henestroza, Lauren Wilcox.
Affirmative Cue Words in Dialogue
In speech, single affirmative cue words such as okay, right and yes
are often used with different functions, including acknowledgment
(meaning "I believe/agree with what you said"), backchannel (indicating
"I'm still here" or "I hear you and please continue"), and beginning of a new
discourse segment (as in "okay, now I will talk about...").
In this project, we analyze how such functions are conveyed
and perceived, and explore how they can be automatically predicted
with Machine Learning algorithms.
Participants: Julia Hirschberg (Columbia University); Stefan Benus (Constantine The Philosopher University); Agustín Gravano (University of Buenos Aires), Lauren Wilcox, Héctor Chávez, Ilia Vovsha (Columbia University); Shira Mitchell (Harvard University).
Intonational Overload: Uses of the Downstepped Contours
Intonational contours are overloaded, conveying different meanings in different
contexts. We are studying potential uses of the downstepped contours (especially H* !H* L- L%)
in Standard American English, both in read and spontaneous speech. We are investigating
speakers' use of these contours in conveying discourse topic structure and in
signaling given vs. new information, and the relationship between these two
functions. We designed and collected the
Columbia Games Corpus especifically for this project.
Participants: Julia Hirschberg (Columbia University); Agustín Gravano (University of Buenos Aires); Gregory Ward, Elisa Sneed (Northwestern University); Stefan Benus (Constantine The Philosopher University), Ani Nenkova, Michael Mulley.
Characterizing Laughter in Dialogue
Laughter can serve many different purposes in human communication and
occurs in many different forms. This project involved studying the acoustic
characteristics of laughter and the functions different types of
laughter may serve in human dialogue and in spoken dialogue systems.
Participants: Brianne Calandra, Rolf Carlson (KTH, Sweden).