A longitudinal study of prosodic exaggeration in child-directed speech

We investigate the role of prosody in child-directed speech of three English speaking adults using data collected for the Human Speechome Project, an ecologically valid, longitudinal corpus collected from the home of a family with a young child. We looked at differences in prosody between child-directed and adult-directed speech. We also looked at the change in prosody of child-directed speech as the child gets older. Results showed significant interactions between speech type and vowel duration, mean F0 and F0 range. We also found significant changes in prosody in child-directed speech as the child gets older

A Human-Machine Collaborative System for Identifying Rumors on Twitter

The spread of rumors on social media, especially in time-sensitive situations such as real-world emergencies, can have harmful effects on individuals and society. In this work, we developed a human-machine collaborative system on Twitter for fast identification of rumors about real-world events. The system reduces the amount of information that users have to sift through in order to identify rumors about real-world events by several orders of magnitude

An automatic child-directed speech detector for the study of child language development

http://interspeech2012.org/accepted-abstract.html?id=210In this paper, we present an automatic child-directed speech detection system to be used in the study of child language development. Child-directed speech (CDS) is speech that is directed by caregivers towards infants. It is not uncommon for corpora used in child language development studies to have a combination of CDS and non-CDS. As the size of the corpora used in these studies grow, manual annotation of CDS becomes impractical. Our automatic CDS detector addresses this issue. The focus of this paper is to propose and evaluate different sets of features for the detection of CDS, using several offthe-shelf classifiers. First, we look at the performance of a set of acoustic features. We continue by combining these acoustic features with several linguistic and eventually contextual features. Using the full set of features, our CDS detector was able to correctly identify CDS with an accuracy of.88 and F1 score of.87 using Naive Bayes. Index Terms: motherese, automatic, child-directed speech, infant-directed speech, adult-directed speech, prosody, language development

Enhanced Twitter Sentiment Classification Using Contextual Information

The rise in popularity and ubiquity of Twitter has made sentiment analysis of tweets an important and well-covered area of research. However, the 140 character limit imposed on tweets makes it hard to use standard linguistic methods for sentiment classification. On the other hand, what tweets lack in structure they make up with sheer volume and rich metadata. This metadata includes geolocation, temporal and author information. We hypothesize that sentiment is dependent on all these contextual factors. Different locations, times and authors have different emotional valences. In this paper, we explored this hypothesis by utilizing distant supervision to collect millions of labelled tweets from different locations, times and authors. We used this data to analyse the variation of tweet sentiments across different authors, times and locations. Once we explored and understood the relationship between these variables and sentiment, we used a Bayesian approach to combine these variables with more standard linguistic features such as n-grams to create a Twitter sentiment classifier. This combined classifier outperforms the purely linguistic classifier, showing that integrating the rich contextual information available on Twitter into sentiment classification is a promising direction of research.Twitter (Firm

Grounding language models in spatiotemporal context

Natural language is rich and varied, but also highly structured. The rules of grammar are a primary source of linguistic regularity, but there are many other factors that govern patterns of language use. Language models attempt to capture linguistic regularities, typically by modeling the statistics of word use, thereby folding in some aspects of grammar and style. Spoken language is an important and interesting subset of natural language that is temporally and spatially grounded. While time and space may directly contribute to a speaker’s choice of words, they may also serve as indicators for communicative intent or other contextual and situational factors. To investigate the value of spatial and temporal information, we build a series of language models using a large, naturalistic corpus of spatially and temporally coded speech collected from a home environment. We incorporate this extralinguistic information by building spatiotemporal word classifiers that are mixed with traditional unigram and bigram models. Our evaluation shows that both perplexity and word error rate can be significantly improved by incorporating this information in a simple framework. The underlying principles of this work could be applied in a wide range of scenarios in which temporal or spatial information is available

Automatic Estimation of Transcription Accuracy and Difficulty

Managing a large-scale speech transcription task with a team of human transcribers requires effective quality control and workload distribution. As it becomes easier and cheaper to collect massive audio corpora the problem is magnified. Relying on expert review or transcribing all speech multiple times is impractical. Furthermore, speech that is difficult to transcribe may be better handled by a more experienced transcriber or skipped entirely. We present a fully automatic system to address these issues. First, we use the system to estimate transcription accuracy from a a single transcript and show that it correlates well with intertranscriber agreement. Second, we use the system to estimate the transcription “difficulty” of a speech segment and show that it is strongly correlated with transcriber effort. This system can help a transcription manager determine when speech segments may require review, track transcriber performance, and efficiently manage the transcription process

TweetVista: An AI-Powered Interactive Tool for Exploring Conversations on Twitter

We present TweetVista, an interactive web-based tool for mapping the conversation landscapes on Twitter. TweetVista is an intelligent and interactive desktop web application for exploring the conversation landscapes on Twitter. Given a dataset of tweets, the tool uses advanced NLP techniques using deep neural networks and a scalable clustering algorithm to map out coherent conversation clusters. The interactive visualization engine then enables the users to explore these clusters. We ran three case studies using datasets about the 2016 US presidential election and the summer 2016 Orlando shooting. Despite the enormous size of these datasets, using TweetVista users were able to quickly and clearly make sense of the various conversation topics around these datasets

A portable audio/video recorder for longitudinal study of child development

Collection and analysis of ultra-dense, longitudinal observational data of child behavior in natural, ecologically valid, non-laboratory settings holds significant promise for advancing the understanding of child development and developmental disorders such as autism. To this end, we created the Speechome Recorder - a portable version of the embedded audio/video recording technology originally developed for the Human Speechome Project - to facilitate swift, cost-effective deployment in home environments. Recording child behavior daily in these settings will enable detailed study of developmental trajectories in children from infancy through early childhood, as well as typical and atypical dynamics of communication and social interaction as they evolve over time. Its portability makes possible potentially large-scale comparative study of developmental milestones in both neurotypical and developmentally delayed children. In brief, the Speechome Recorder was designed to reduce cost, complexity, invasiveness and privacy issues associated with naturalistic, longitudinal recordings of child development.National Institutes of Health (U.S.) (Grant R01 2DC007428)Nancy Lurie Marks Family Foundatio

Human Atlas: A Tool for Mapping Social Networks

Most social network analyses focus on online social networks. While these networks encode important aspects of our lives they fail to capture many real-world connections. Most of these connections are, in fact, public and known to the members of the community. Mapping them is a task very suitable for crowdsourcing: it is easily broken down in many simple and independent subtasks. Due to the nature of social networks|presence of highly connected nodes and tightly knit groups|if we allow users to map their immediate connections and the connections between them, we will need few participants to map most connections within a community. To this end, we built the Human Atlas, a web-based tool for mapping social networks. To test it, we partially mapped the social network of the MIT Media Lab. We ran a user study and invited members of the community to use the tool. In 4.6 man-hours, 22 participants mapped 984 connections within the lab, demonstrating the potential of the tool

Contributions of Prosodic and Distributional Features of Caregivers' Speech in Early Word Learning

How do characteristics of caregiver speech contribute to a child's early word learning? We explore the relationship between a single child's vocabulary growth and the distributional and prosodic characteristics of the speech he hears using data collected for the Human Speechome Project, an ecologically valid corpus collected from the home of a family with a young child. We measured F0, intensity, phoneme duration, usage frequency, recurrence, and MLU for caregivers' production of each word that the child learned during the period of recording. When all variables are considered, we obtain a model of word acquisition as a function of caregiver input speech. Coefficient estimates in the model help to illuminate which factors are relevant to learning classes of words. In addition, words that deviate from the model's prediction are of interest as they may suggest important social, contextual and other cues relevant to word learning