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. This paper presents an initial study using n-best recognition hypotheses for two tasks, extractive summarization and keyword extraction. We extend the approach used on 1-best output to n-best hypotheses: MMR (maximum marginal relevance) for summarization and TFIDF (term frequency, inverse document frequency) weighting for
This paper describes experiments for audio clips comparison based on spoken context. The spoken content is obtained using automatic speech recognition. The social tags that are available for most of the audio clips are used as keywords. These keywords are mapped to the spoken transcription representing the audio clips
results in up to 1.1% absolute Word Error Rate (WER) improvement as compared to keyword-based approaches. The proposed approach reduces the WER by 6.3% absolute in our experiments, compared to an in-domain LM without considering any Web data.
General purpose search engines provide users with lists of retrieved documents in response to their queries. The common structure of list elements includes the title of a document, its URL, and small snippet from the text. Snippets are evidence of occurrences of query's keywords in the document. The length of each
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.