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As speech based emotion recognition has matured to a degree where it becomes applicable within real-life conditions, it is time for a realistic view on obtainable performances. Most state-of-the-art emotion recognition methods are based on turn- and frame-level analysis independent of phonetic transcription. True speaker disjoint partitioning of training and test sets is still less common than simple...
Most state-of-the-art emotion recognition methods are based on turn- and frame-level analysis independent from phonetic transcription. Currently "affective computing" community could not specify the smallest emotional standard unit which can be easily classified and determined by any "advanced" and "non-advanced" listener. It is known that, acoustic modeling on the smallest...
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