Overview of VideoCLEF 2009: New perspectives on speech-based multimedia content enrichment

VideoCLEF 2009 offered three tasks related to enriching video content for improved multimedia access in a multilingual environment. For each task, video data (Dutch-language television, predominantly documentaries) accompanied by speech recognition transcripts were provided. The Subject Classification Task involved automatic tagging of videos with subject theme labels. The best performance was achieved by approaching subject tagging as an information retrieval task and using both speech recognition transcripts and archival metadata. Alternatively, classifiers were trained using either the training data provided or data collected from Wikipedia or via general Web search. The Affect Task involved detecting narrative peaks, defined as points where viewers perceive heightened dramatic tension. The task was carried out on the “Beeldenstorm” collection containing 45 short-form documentaries on the visual arts. The best runs exploited affective vocabulary and audience directed speech. Other approaches included using topic changes, elevated speaking pitch, increased speaking intensity and radical visual changes. The Linking Task, also called “Finding Related Resources Across Languages,” involved linking video to material on the same subject in a different language. Participants were provided with a list of multimedia anchors (short video segments) in the Dutch-language “Beeldenstorm” collection and were expected to return target pages drawn from English-language Wikipedia. The best performing methods used the transcript of the speech spoken during the multimedia anchor to build a query to search an index of the Dutch language Wikipedia. The Dutch Wikipedia pages returned were used to identify related English pages. Participants also experimented with pseudo-relevance feedback, query translation and methods that targeted proper names

Cross-Linguistic Influence in the Bilingual Mental Lexicon: Evidence of Cognate Effects in the Phonetic Production and Processing of a Vowel Contrast.

The present study examines cognate effects in the phonetic production and processing of the Catalan back mid-vowel contrast (/o/-/ɔ/) by 24 early and highly proficient Spanish-Catalan bilinguals in Majorca (Spain). Participants completed a picture-naming task and a forced-choice lexical decision task in which they were presented with either words (e.g., /bɔsk/ "forest") or non-words based on real words, but with the alternate mid-vowel pair in stressed position ((*)/bosk/). The same cognate and non-cognate lexical items were included in the production and lexical decision experiments. The results indicate that even though these early bilinguals maintained the back mid-vowel contrast in their productions, they had great difficulties identifying non-words and real words based on the identity of the Catalan mid-vowel. The analyses revealed language dominance and cognate effects: Spanish-dominants exhibited higher error rates than Catalan-dominants, and production and lexical decision accuracy were also affected by cognate status. The present study contributes to the discussion of the organization of early bilinguals' dominant and non-dominant sound systems, and proposes that exemplar theoretic approaches can be extended to include bilingual lexical connections that account for the interactions between the phonetic and lexical levels of early bilingual individuals

A Robust Transformation-Based Learning Approach Using Ripple Down Rules for Part-of-Speech Tagging

In this paper, we propose a new approach to construct a system of transformation rules for the Part-of-Speech (POS) tagging task. Our approach is based on an incremental knowledge acquisition method where rules are stored in an exception structure and new rules are only added to correct the errors of existing rules; thus allowing systematic control of the interaction between the rules. Experimental results on 13 languages show that our approach is fast in terms of training time and tagging speed. Furthermore, our approach obtains very competitive accuracy in comparison to state-of-the-art POS and morphological taggers.Comment: Version 1: 13 pages. Version 2: Submitted to AI Communications - the European Journal on Artificial Intelligence. Version 3: Resubmitted after major revisions. Version 4: Resubmitted after minor revisions. Version 5: to appear in AI Communications (accepted for publication on 3/12/2015

Technology Policy, Gender, and Cyberspace

Event based sampling occurs when the time instants are measured everytime the amplitude passes certain pre-defined levels. This is in contrast with classical signal processing where the amplitude is measured at regular time intervals. The signal processing problem is to separate the signal component from noise in both amplitude and time domains. Event based sampling occurs in a variety of applications. The purpose here is to explain the new types of signal processing problems that occur, and identify the need for processing in both the time and event domains. We focus on rotating axles, where amplitude disturbances are caused by vibrations and time disturbances from measurement equipment. As one application, we examine tire pressure monitoring in cars where suppression of time disturbance is of utmost importance

Object Referring in Visual Scene with Spoken Language

Object referring has important applications, especially for human-machine interaction. While having received great attention, the task is mainly attacked with written language (text) as input rather than spoken language (speech), which is more natural. This paper investigates Object Referring with Spoken Language (ORSpoken) by presenting two datasets and one novel approach. Objects are annotated with their locations in images, text descriptions and speech descriptions. This makes the datasets ideal for multi-modality learning. The approach is developed by carefully taking down ORSpoken problem into three sub-problems and introducing task-specific vision-language interactions at the corresponding levels. Experiments show that our method outperforms competing methods consistently and significantly. The approach is also evaluated in the presence of audio noise, showing the efficacy of the proposed vision-language interaction methods in counteracting background noise.Comment: 10 pages, Submitted to WACV 201