EMOTION RECOGNITION BY SPEECH SIGNAL

By Oh-Wook Kwon, Kwokleung Chan, Jiucang Hao. 2003

308 citations recorded by the source corpus.

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Citations in the corpus

Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion? (2019, background): “Indeed, state-of-the-art speech processing algorithms can infer not only the spoken contents from the speech signal, but also the speaker’s identity [5], intention [6–9], gender [10, 11], emotional state [12–14], pathological condition [15–17], personality [18, 19] and cultural [20, 21] attributes t”
Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge (2011, methodology): “…used since the very ﬁrst studies (Dellaert et al., 1996; Petrushin, 1999) and turned out to be quite successful for non-acted emotional speech as well (Batliner et al., 2000; Kwon et al., 2003; Litman and Forbes, 2003; Rahur-kar and Hansen, 2003; Lee and Narayanan, 2005; Shami and Verhelst, 2007).”
Automatic analysis of multimodal group actions in meetings (2005, background): “Recent research in recognizing emotion from speech [33], [34], recognizing interest level from posture [35], recognizing hot-spots (regions of high involvement or emphasis) in meetings [36], [37], [38], and detecting agreement and disagreement in meetings [39], suggests that the automatic recognitio”
Databases, features and classifiers for speech emotion recognition: a review (2018, background): “Kwon et al. (2003) explored log energy, formants, F0 information, mel based energy, MFCCs with their velocity and acceleration coefficient for speech emotion recognition.”
Self-Supervised ECG Representation Learning for Emotion Recognition (2020, background): “ty recognition [5,11], and others. A wide variety of data sources have been utilized for affective computing purposes. These data sources include facial expressions [12], motion and gait [13], speech [14], electrocardiogram (ECG) [15], electroencephalogram (EEG) [6], electrooculogram (EOG) [16], and”
Artificial intelligence in the creative industries: a review (2020, unknown): “tone, loudness, and tempo) [281], face detection (e.”
Comparison of Gender- and Speaker-adaptive Emotion Recognition (2014, background): “One of the pilot experiments which deals with speech based emotion recognition has been presented by Kwon et al. (2003).”
Emotion recognition from speech: a review (2012, unknown): “Along with F0 information, log energy, formants, mel based energy, MFCCs with their velocity and acceleration coefficients are explored for emotion classification (Kwon et al. 2003).”
Features and classifiers for emotion recognition from speech: a survey from 2000 to 2011 (2012, methodology): “To this end, traditional segmental features like MFCCs or LPCCs are transformed to suprasegmental parameterizations through long-time statistic processing, in order to be concatenated to the prosodic feature vector (Kwon et al. 2003; Vogt and André 2006; Schuller et al. 2005a).”