Prominence detected by listeners for future speech synthesis application

Proceedings of the 17th Nordic Conference of Computational Linguistics NODALIDA 2009. Editors: Kristiina Jokinen and Eckhard Bick. NEALT Proceedings Series, Vol. 4 (2009), 251-254. © 2009 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/9206

DCU at MediaEval 2011: Rich Speech Retrieval (RSR)

We describe our runs and results for the Rich Speech Re- trieval (RSR) Task at MediaEval 2011. Our runs examine the use of alternative segmentation methods on the provided ASR transcripts to locate the beginning of the topic, assum- ing that this will capture or get close to the starting point of the relevant segment; combination of various types of queries and weighting of metadata to move the relevant segment higher in the ranked list; and dierent ASR transcripts to compare the in uence of the ASR transcripts quality. Our results show that newer versions of the transcripts and use of metadata produce better results on average. So far we have not used information about the illocutionary act type corresponding to each query, but analysis of the retrieval results shows dierence in behaviour for queries associated with certatin classes of act

DCU search runs at MediaEval 2012: search and hyperlinking task

We describe the runs for our participation in the Search sub-task of the Search and Hyperlinking Task at MediaEval 2012. Our runs are designed to form a retrieval baseline by using time-based segmentation of audio transcripts incorporating pause information and a sliding window to define the retrieval segments boundaries with a standard language modelling information retrieval strategy. Using this baseline system runs based on transcripts provided by LIUM were better for all evaluation metrics, than those using transcripts provided by LIMSI

DCU at the NTCIR-9 spokendoc passage retrieval task

We describe details of our runs and the results obtained for the "IR for Spoken Documents (SpokenDoc) Task" at NTCIR-9. The focus of our participation in this task was the investigation of the use of segmentation methods to divide the manual and ASR transcripts into topically coherent segments. The underlying assumption of this approach is that these segments will capture passages in the transcript relevant to the query. Our experiments investigate the use of two lexical coherence based segmentation algorithms (Text-Tiling, C99). These are run on the provided manual and ASR transcripts, and the ASR transcript with stop words removed. Evaluation of the results shows that TextTiling consistently performs better than C99 both in segmenting the data into retrieval units as evaluated using the centre located relevant information metric and in having higher content precision in each automatically created segment

Creating a data collection for evaluating rich speech retrieval

We describe the development of a test collection for the investigation of speech retrieval beyond identification of relevant content. This collection focuses on satisfying user information needs for queries associated with specific types of speech acts. The collection is based on an archive of the Internet video from Internet video sharing platform (blip.tv), and was provided by the MediaEval benchmarking initiative. A crowdsourcing approach was used to identify segments in the video data which contain speech acts, to create a description of the video containing the act and to generate search queries designed to refind this speech act. We describe and reflect on our experiences with crowdsourcing this test collection using the Amazon Mechanical Turk platform. We highlight the challenges of constructing this dataset, including the selection of the data source, design of the crowdsouring task and the specification of queries and relevant items

Adapting Binary Information Retrieval Evaluation Metrics for Segment-based Retrieval Tasks

This report describes metrics for the evaluation of the effectiveness of segment-based retrieval based on existing binary information retrieval metrics. This metrics are described in the context of a task for the hyperlinking of video segments. This evaluation approach re-uses existing evaluation measures from the standard Cranfield evaluation paradigm. Our adaptation approach can in principle be used with any kind of effectiveness measure that uses binary relevance, and for other segment-baed retrieval tasks. In our video hyperlinking setting, we use precision at a cut-off rank n and mean average precision.Comment: Explanation of evaluation measures for the linking task of the MediaEval Workshop 201

Time-based segmentation and use of jump-in points in DCU search runs at the search and hyperlinking task at MediaEval 2013

We describe the runs for our participation in the Search sub-task of the Search and Hyperlinking Task at MediaEval 2013. Our experiments investigate the aect of using information about speech segment boundaries and pauses on the effectiveness of retrieving jump-in points within the retrieved segments. We segment all three available types of transcripts (automatic ones provided by LIMSI/Vocapia and LIUM, and manual subtitles provided by BBC) into fixed-length time units, and present the resulting runs using the original segment starts and using the potential jump-in points. Our method for adjustment of the jump-in points achieves higher scores for all LIMSI/Vocapia, LIUM, and subtitles based runs

Overview of MediaEval 2011 rich speech retrieval task and genre tagging task

The MediaEval 2011 Rich Speech Retrieval Tasks and Genre Tagging Tasks are two new tasks oered in MediaEval 2011 that are designed to explore the development of techniques for semi-professional user generated content (SPUG). They both use the same data set: the MediaEval 2010 Wild Wild Web Tagging Task (ME10WWW). The ME10WWW data set contains Creative Commons licensed video collected from blip.tv in 2009. It was created by the PetaMedia Network of Excellence (http://www.petamedia.eu) in order to test retrieval algorithms for video content as it occurs `in the wild' on the Internet and, in particular, for user contributed multimedia that is embedded within a social network. In this overview paper, we repeat the essential characteristics of the data set, describe the tasks and specify how they are evaluated

Search and hyperlinking task at MediaEval 2012

The Search and Hyperlinking Task was one of the Brave New Tasks at MediaEval 2012. The Task consisted of two subtasks which focused on search and linking in retrieval from a collection of semi-professional video content. These tasks followed up on research carried out within the MediaEval 2011 Rich Speech Retrieval (RSR) Task and the VideoCLEF 2009 Linking Task

The search and hyperlinking task at MediaEval 2013

The Search and Hyperlinking Task formed part of the MediaEval 2013 evaluation workshop. The Task consisted of two sub-tasks: (1) answering known-item queries from a collection of roughly 1200 hours of broadcast TV material, and (2) linking anchors within the known item to other parts of the video collection. We provide an overview of the task and the data sets used