Emotional speech recognition: Resources, features, and methods

By Dimitrios Ververidis, Constantine Kotropoulos. Speech Communication, 2006

1,048 citations recorded by the source corpus.

Subfields: Experimental and Cognitive Psychology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/8bc9630d2bf6b3fc912916c8eef2c0dc459a6bf7

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Citations in the corpus

Emotion recognition from speech: a review (2012, methodology): “Short time supra-segmental features such as pitch, energy, formant locations and their bandwidths, dynamics of pitch, energy and formant contours, speaking rate are used for analyzing the emotions (Ververidis and Kotropoulos 2006).”
The Vera am Mittag German audio-visual emotional speech database (2008, background): “Although in recent years much effort has been invested in collecting and segmenting audio-visual speech data [1, 2, 3, 4], there is still a lack of spontaneous and emotionally rich speech corpora.”
Evaluating deep learning architectures for Speech Emotion Recognition (2017, methodology): “SER can be regarded as a static or dynamic classification problem, which has motivated two popular formulations in the literature to the task (Ververidis & Kotropoulos, 2006): turn-based processing (also known as static modeling), which aims to recognize emotions from a complete utterance; or…”
Speech Emotion Recognition Using Deep Learning Techniques: A Review (2019, neutral): “and gets low with low pitch [33].”
The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing (2016, neutral): “Index Terms— Affective computing, acoustic features, standard, emotion recognition, speech analysis, geneva minimalistic parameter set Ç”
Databases, features and classifiers for speech emotion recognition: a review (2018, background): “There are three kinds of databases which are used for development of speech emotion recognition systems—natural, simulated (Acted) and elicited (Induced) emotional speech database (Ververidis and Kotropoulos 2006).”
Short-term emotion assessment in a recall paradigm (2009, background): “, 2001; Fasel and Luettin, 2003), speech (Cowie, 2000; Ververidis and Kotropoulos, 2006), postures (Coulson, 2004; Kapoor et al.”
Speech emotion recognition (2018, neutral): “The current trend is to increase the number of features up to some several thousands of bruteforced features that was often in stark contrast to the sparse amount of training material available in this field.(17,38,43) Textual features.”
Automatic speech emotion recognition using modulation spectral features (2011, background): “…information conveyed by speech emotions has been found to be useful in multiple ways in speech processing, especially serving as an important ingredient of " emotional intelligence " of machines and contributing to human– machine interaction (Cowie et al., 2001; Ververidis and Kotropoulos, 2006).”
A review of verbal and non-verbal human-robot interactive communication (2014, background): “Regarding some basic supporting technologies of affect-enabled text-to-speech and speech recognition, the interested reader can refer to the general reviews by Schroeder [117] on TTS, and by Ververidis and Kotropoulos [118] on recognition.”
Can We Generate Emotional Pronunciations for Expressive Speech Synthesis? (2020, neutral): “There are mainly three types of emotional databases in the literature [9].”
Learning Salient Features for Speech Emotion Recognition Using Convolutional Neural Networks (2014, methodology): “In the following, we first briefly review the classification strategies, and then focus on feature extraction methods, as they are more related to this work.”