Primitives-based evaluation and estimation of emotions in speech

By Michael Grimm, Kristian Kroschel, Emily Mower Provost, Shrikanth S. Narayanan. Speech Communication, 2007

333 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/9b4b7cf1706b585eb8baed1764a404aca8a47742

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Citations in the corpus

IEMOCAP: interactive emotional dyadic motion capture database (2008, methodology): “The self-assessment manikins (SAMs) were used to evaluate the corpus in terms of the attributes valence [1-negative, 5-possitive], activation [1-calm, 5-excited], and dominance [1-weak, 5-strong] [33, 37] (Fig.”
“Love ya, jerkface”: Using Sparse Log-Linear Models to Build Positive and Impolite Relationships with Teens (2012, background): “Although speaker state prediction has attracted much attention in the dialogue research community, most studies have focused on the analysis of anger, frustration, and other classic emotions (Litman and Forbes-Riley, 2004; Liscombe et al., 2005; Devillers and Vidrascu, 2006; Ai et al., 2006; Grimm e”
Analysis of Emotionally Salient Aspects of Fundamental Frequency for Emotion Detection (2009, background): “Note that the EMA data have been perceptually evaluated and inter-evaluator agreement has been shown to be 81.9% [22].”
Automatic speech emotion recognition using modulation spectral features (2011, methodology): “Prosodic features have been, among numerous acoustic features employed for SER, the most widely used feature type as mentioned in Section 1.”
The Vera am Mittag German audio-visual emotional speech database (2008, methodology): “Our major interest when collecting this database was the recognition of the emotion conveyed in the dialogues [5, 6].”
Speech Emotion Recognition Using Fourier Parameters (2015, methodology): “The mean of H 3 for speech signals with different emotions from the German and Chinese databases. the mean, maximum, minimum, median and standard deviation of the amplitudes of the first 20 Fourier parameters are calculated as in [1] , [7], [15], [46] and [47].”
Building Naturalistic Emotionally Balanced Speech Corpus by Retrieving Emotional Speech from Existing Podcast Recordings (2019, methodology): “We use self-assessment manikins (SAMs) [38], [39] to visually guide the evaluators in annotating these dimensional attributes.”