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Automatic speech recognition (ASR) for low resource languages continues to be a difficult problem. In particular, colloquial dialects of Arabic, Farsi, and Pashto pose significant challenges in pronunciation dictionary creation. Therefore, most state-of-the-art ASR engines rely on the grapheme-as-phoneme approach for creating pronunciation dictionaries in these languages. While the grapheme approach...
This paper presents the second version of ESEDA, a speech emotion recognition tool. The current version of the tool has a number of novel capabilities as compared to the first version ESEDA.0 and other speech emotion recognition systems: firstly, it incorporates a novel classification method TGI+, and secondly, it includes a module for high level feature extraction. These two functionalities have...
In the last decade, the efforts of spoken language processing have achieved significant advances, however, the work with emotional recognition has not progressed so far, and can only achieve 50% to 60% in accuracy. This is because a majority of researchers in this field have focused on the synthesis of emotional speech rather than focusing on automating human emotion recognition. Many research groups...
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