A Semi-automatic Method for Efficient Detection of Stories on Social Media

Twitter has become one of the main sources of news for many people. As real-world events and emergencies unfold, Twitter is abuzz with hundreds of thousands of stories about the events. Some of these stories are harmless, while others could potentially be life-saving or sources of malicious rumors. Thus, it is critically important to be able to efficiently track stories that spread on Twitter during these events. In this paper, we present a novel semi-automatic tool that enables users to efficiently identify and track stories about real-world events on Twitter. We ran a user study with 25 participants, demonstrating that compared to more conventional methods, our tool can increase the speed and the accuracy with which users can track stories about real-world events.Comment: ICWSM'16, May 17-20, Cologne, Germany. In Proceedings of the 10th International AAAI Conference on Weblogs and Social Media (ICWSM 2016). Cologne, German

Context-aware person identification in personal photo collections

Identifying the people in photos is an important need for users of photo management systems. We present MediAssist, one such system which facilitates browsing, searching and semi-automatic annotation of personal photos, using analysis of both image content and the context in which the photo is captured. This semi-automatic annotation includes annotation of the identity of people in photos. In this paper, we focus on such person annotation, and propose person identification techniques based on a combination of context and content. We propose language modelling and nearest neighbor approaches to context-based person identification, in addition to novel face color and image color content-based features (used alongside face recognition and body patch features). We conduct a comprehensive empirical study of these techniques using the real private photo collections of a number of users, and show that combining context- and content-based analysis improves performance over content or context alone

Jitter and Shimmer measurements for speaker diarization

Jitter and shimmer voice quality features have been successfully used to characterize speaker voice traits and detect voice pathologies. Jitter and shimmer measure variations in the fundamental frequency and amplitude of speaker's voice, respectively. Due to their nature, they can be used to assess differences between speakers. In this paper, we investigate the usefulness of these voice quality features in the task of speaker diarization. The combination of voice quality features with the conventional spectral features, Mel-Frequency Cepstral Coefficients (MFCC), is addressed in the framework of Augmented Multiparty Interaction (AMI) corpus, a multi-party and spontaneous speech set of recordings. Both sets of features are independently modeled using mixture of Gaussians and fused together at the score likelihood level. The experiments carried out on the AMI corpus show that incorporating jitter and shimmer measurements to the baseline spectral features decreases the diarization error rate in most of the recordings.Peer ReviewedPostprint (published version

Speaker segmentation and clustering

This survey focuses on two challenging speech processing topics, namely: speaker segmentation and speaker clustering. Speaker segmentation aims at finding speaker change points in an audio stream, whereas speaker clustering aims at grouping speech segments based on speaker characteristics. Model-based, metric-based, and hybrid speaker segmentation algorithms are reviewed. Concerning speaker clustering, deterministic and probabilistic algorithms are examined. A comparative assessment of the reviewed algorithms is undertaken, the algorithm advantages and disadvantages are indicated, insight to the algorithms is offered, and deductions as well as recommendations are given. Rich transcription and movie analysis are candidate applications that benefit from combined speaker segmentation and clustering. © 2007 Elsevier B.V. All rights reserved