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• W1517832746 abstract "Speech is the most natural form of expression which is why it accounts for the majority of communication and information around the world. Media monitoring is a crucial activity today. For the most part today’s methods are manual, with human reading, listening and watching, annotating topics and selecting items of interest for the user. The huge amount of data we can access nowadays in different formats (audio, video, text) and through distinct channels revealed the necessity to build systems that can efficiently store this data and retrieve this data automatically. Unavoidable component of such systems is speech recognition engine. Different types of speech and speech environments pose different challenges and, therefore, require different engines to accurately process the speech. Speech recognition of broadcast news (BN ASR) is designed for news-oriented content from either television or radio and it readily processes broadcasts that include news, multispeaker roundtable discussions and debates and even open-air interviews outside of the studio. BN ASR is a challenging task for many years and different languages. This chapter summarizes our key efforts to build BN ASR system for Slovenian language. BN ASR system open the possibility for many applications where the use of automatic transcriptions is a major attribute. One of applications is live subtitling (Brousseau et al., 2003; Imai et al., 2000; Lambourne et al., 2004), were BN ASR system processes audio input and creates closed captions (Figure 1). Another task is speaker tracking, which can be used to find parts of speech belonging to specific speaker (Leggetter et al., 1995) in an audio input (Figure 2). Speech content search and retrieval is also a very useful functionality, which can be applied based on speech recognition. Based on some key terms a user can index audio/video to create a searchable repository to find the exact clip they need and its transcript. Yet another challenging field is topic detection and topic tracking. The goal is to use the system for continuously monitoring a TV channel, and searching inside their news programs for the stories that match the profile of a given user. The chapter describes in detail our Speech Recognition System of Slovenian Broadcast News (UMB BNSI system), which is still under development. The chapter is organized as follows. First in section 2 we overview research work on broadcast news speech recognition. Properties of the Slovenian language make transcribing Slovenian broadcast news a more challenging task than for example English language. In section 3 basic differences are outlined. Section 4 summarizes the speech and text corpora used for training and testing the" @default.
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• W1517832746 date "2011-06-23" @default.
• W1517832746 modified "2023-09-30" @default.
• W1517832746 title "Speech Recognition System of Slovenian Broadcast News" @default.
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• W1517832746 doi "https://doi.org/10.5772/17161" @default.
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