Detail publikačního výsledku

An Automatic Emotion Recognizer using MFCCs and Hidden Markov Models

VYAS, G.; DUTTA, M.; ŘÍHA, K.; PŘINOSIL, J.

Originální název

An Automatic Emotion Recognizer using MFCCs and Hidden Markov Models

Anglický název

An Automatic Emotion Recognizer using MFCCs and Hidden Markov Models

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

In this paper, the proficiency of continuous Hidden Markov Models to recognize emotions from speech signals has been investigated. Unlike the existing work which considers prosodic features for automatic emotion recognition, this work proposes the effectiveness of the phonetic features of speech particularly, Mel-Frequency Cepstral Coefficients which improves the accuracy with reduced feature set. The continuous speech emotional utterances used in this work have been taken from the SAVEE emotional corpus. The Hidden Markov Model Toolkit (HTK) version 3.4.1 was utilized for extraction of the acoustic features as well as generation of the models. Optimizing the acoustic and pre-processing parameters along with the number of states and transition probabilities of the Markov Models, the trials give us an average accuracy of 78% and highest accuracy of 91.25% for four emotions sadness, surprise, fear and disgust.

Anglický abstrakt

In this paper, the proficiency of continuous Hidden Markov Models to recognize emotions from speech signals has been investigated. Unlike the existing work which considers prosodic features for automatic emotion recognition, this work proposes the effectiveness of the phonetic features of speech particularly, Mel-Frequency Cepstral Coefficients which improves the accuracy with reduced feature set. The continuous speech emotional utterances used in this work have been taken from the SAVEE emotional corpus. The Hidden Markov Model Toolkit (HTK) version 3.4.1 was utilized for extraction of the acoustic features as well as generation of the models. Optimizing the acoustic and pre-processing parameters along with the number of states and transition probabilities of the Markov Models, the trials give us an average accuracy of 78% and highest accuracy of 91.25% for four emotions sadness, surprise, fear and disgust.

Klíčová slova

Emotion; recognition; HTK toolkit; Mel frequency cepstral coefficients

Klíčová slova v angličtině

Emotion; recognition; HTK toolkit; Mel frequency cepstral coefficients

Autoři

VYAS, G.; DUTTA, M.; ŘÍHA, K.; PŘINOSIL, J.

Rok RIV

2016

Vydáno

08.10.2015

Místo

Brno, Czech Republic

ISBN

978-1-4673-9282-2

Kniha

2015 7th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

Strany od

320

Strany do

324

Strany počet

5

BibTex

@inproceedings{BUT117824,
  author="Garima {Vyas} and Malay Kishore {Dutta} and Kamil {Říha} and Jiří {Přinosil}",
  title="An Automatic Emotion Recognizer using MFCCs and Hidden Markov Models",
  booktitle="2015 7th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)",
  year="2015",
  pages="320--324",
  address="Brno, Czech Republic",
  isbn="978-1-4673-9282-2"
}