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Keyword spotting becomes a very important branch of speech recognition. But the acoustic mismatch between training and testing environments often causes a severe degradation in the recognition performance. This paper presents an improved keyword spotting strategy. A fuzzy search algorithm is proposed to extract
, the system carry out conversation with the user to explicitly understand his/her needs and accordingly filters search results for display. The conversation between the system and the user is based on word co-occurrence keyword extraction and Artificial Intelligence Markup Language (AIML) technique. As per initial
The paper presents unsupervised method for word detection in recorded spoken language signal. The method is based on examining signal similarity of two analyzed media description: registered voice and a word (textual query) synthesized by using Text-to-Speech tools. The descriptions of media were given by a sequence of Mel-Frequency Cepstral Coefficients or Human-Factor Cepstral Coefficients. Dynamic...
We present an ontological approach for interpreting natural language commands as well as an application to intelligent robots. In our approach, we first extract meaningful keywords from the user commands by adopting lexico-semantic pattern matching. A structured representation is then built by extracting and arranging
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.