Abstract
The deployment and integration of audio processing tools can enhance the semantic annotation of multimedia content, and as a consequence, improve the effectiveness of conceptual access tools. This paper overviews the various ways in which automatic speech and audio analysis can contribute to increased granularity of automatically extracted metadata. A number of techniques will be presented, including the alignment of speech and text resources, large vocabulary speech recognition, key word spotting and speaker classification. The applicability of techniques will be discussed from a media crossing perspective. The added value of the techniques and their potential contribution to the content value chain will be illustrated by the description of two (complementary) demonstrators for browsing broadcast news archives.
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de Jong, F., Ordelman, R., Huijbregts, M. (2006). Automated Speech and Audio Analysis for Semantic Access to Multimedia. In: Avrithis, Y., Kompatsiaris, Y., Staab, S., O’Connor, N.E. (eds) Semantic Multimedia. SAMT 2006. Lecture Notes in Computer Science, vol 4306. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11930334_18
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DOI: https://doi.org/10.1007/11930334_18
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