Language independent search in MediaEval's Spoken Web Search task |
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| Affiliation: | 1. Carnegie Mellon University, Pittsburgh, PA, USA;2. North-West University, Vanderbijlpark, South Africa;3. Telefonica Research, Barcelona, Spain;4. CNRS–IRISA, Rennes, France;1. Department of Mathematics and Computer Science, University of Udine, Udine, Italy;2. Department of Computer Science, University of Verona, Verona, Italy;1. Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland;2. Department of Signal Processing and Acoustics, Aalto University, Espoo, Finland;3. Department of Speech and Hearing Sciences, University of Arizona, AZ, USA;1. King Abdullah University of Science and Technology, Thuwal, Saudi Arabia;2. Stanford University, Stanford, CA, USA;1. Cambridge Research Laboratory, Toshiba Research Europe Limited, 208 Cambridge Science Park, Milton Road, Cambridge CB4 0GZ, UK;2. Corporate Research and Development Center, Toshiba Corporation 1, Komukai Toshiba-cho, Saiwai-ku, Kawasaki 212-8582, Japan;1. School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083, China;2. Department of Mathematics and Statistics, McGill University, Montreal, Quebec, Canada H3A 0B9;3. School of Science, Tianjin Chengjian University, Tianjin 300384, China;4. School of Materials Science and Engineering, Kunming University of Science and Technology, Kunming 650093, China |
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| Abstract: | In this paper, we describe several approaches to language-independent spoken term detection and compare their performance on a common task, namely “Spoken Web Search”. The goal of this part of the MediaEval initiative is to perform low-resource language-independent audio search using audio as input. The data was taken from “spoken web” material collected over mobile phone connections by IBM India as well as from the LWAZI corpus of African languages. As part of the 2011 and 2012 MediaEval benchmark campaigns, a number of diverse systems were implemented by independent teams, and submitted to the “Spoken Web Search” task. This paper presents the 2011 and 2012 results, and compares the relative merits and weaknesses of approaches developed by participants, providing analysis and directions for future research, in order to improve voice access to spoken information in low resource settings. |
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| Keywords: | Low-resource speech technology Evaluation Spoken web Spoken term detection |
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