Audio Based Emotion Recognition Using Mel Frequency Cepstral Coefficient and Support Vector Machine

Nancy, A. Maria; Kumar, G. Senthil; Doshi, Priyal; Shaw, Sanket

doi:10.1166/jctn.2018.7447

Cited by 7 publications

(1 citation statement)

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“…The number of acoustic parameters proven to contain emotional information is still increasing. Generally, the most commonly used features can be divided into three groups: prosodic features (e.g., fundamental frequency, energy, speed of speech) [ 22 ], quality characteristics (e.g., formants, brightness) [ 23 ] and spectrum characteristics (e.g., mel-frequency cepstral coefficients) [ 24 , 25 ]. The final features vector is based on their statistics such as mean, maximum, minimum, change rate, kurtosis, skewness, zero-crossing rate, variance etc., [ 26 , 27 ].…”

Section: Related Workmentioning

confidence: 99%

Emotional Speech Recognition Based on the Committee of Classifiers

Kamińska

2019

Entropy

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This article presents the novel method for emotion recognition from speech based on committee of classifiers. Different classification methods were juxtaposed in order to compare several alternative approaches for final voting. The research is conducted on three different types of Polish emotional speech: acted out with the same content, acted out with different content, and spontaneous. A pool of descriptors, commonly utilized for emotional speech recognition, expanded with sets of various perceptual coefficients, is used as input features. This research shows that presented approach improve the performance with respect to a single classifier.

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