Analysis of Emotionally Salient Aspects of Fundamental Frequency for Emotion Detection

By Carlos Busso, Sungbok Lee, Shrikanth S. Narayanan. IEEE Transactions on Audio, Speech, and Language Processing, 2009

303 citations recorded by the source corpus.

Subfields: Experimental and Cognitive Psychology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/4135aa2f3d892b0a9dfeefa85afe7df20876254e

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In Experimental and Cognitive Psychology

In Signal Processing

Citations in the corpus

Computational Analysis of Persuasiveness in Social Multimedia: A Novel Dataset and Multimodal Prediction Approach (2014, background): “• Pitch (F0), also referred to as the fundamental frequency, is closely tied to the affective aspect of speech [4].”
Automatic speech emotion recognition using modulation spectral features (2011, methodology): “Prosodic features have been shown to deliver important emotional cues of the speaker (Cowie et al., 2001; Ververidis and Kotropoulos, 2006; Busso et al., 2009).”
A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, background): “The prosodic features consist of fundamental frequency (rhythmical and tonal characteristics) [171], energy (volume or the intensity), and duration (sum of time to build vowels, words and similar constructs).”
Survey on speech emotion recognition: Features, classification schemes, and databases (2011, background): “Most researchers believe that prosody continuous features such as pitch and energy convey much of the emotional content of an utterance [29,19,12].”
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].”
Emotion recognition using a hierarchical binary decision tree approach (2011, background): “For example, we know that acoustic features can accurately discriminate between high-activated emotion classes and low-activated emotion classes (Busso et al., 2009).”
The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing (2016, unknown): “Index Terms— Affective computing, acoustic features, standard, emotion recognition, speech analysis, geneva minimalistic parameter set Ç”
Speech Intention Classification with Multimodal Deep Learning (2017, methodology): “…(HSF) using the same data split and softmax configuration was trained on several widely used manufactured features including fundamental frequency [19], pitch related features [20], energy related features [21], zero crossing rate (ZCR) [21, 22], jitter [21], shimmer [21], and Mel-frequency…”