Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
A Multi-hop Energy-Efficient Cluster-Based Routing Using Multi-verse Optimizer in IoT Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Speech Emotion Recognition Using CNN, k-NN, MLP and Random Forest

Authors : Jasmeet Kaur, Anil Kumar

Published in: Computer Networks and Inventive Communication Technologies

Publisher: Springer Nature Singapore

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Emotion recognition from speech has become a hot topic among researchers. This paper describes several methods to recognize emotions from speech signals using machine learning algorithms such as a k-nearest neighbour, multi-layer perceptron, convolutional neural network and random forest. Short-term Fourier transform spectrograms and mel frequency cepstral coefficients were extracted from Berlin database of emotional speech. Spectrograms were used as input for CNN. While MFCC features were input to k-NN, MLP and random forest. Each classifier demonstrated satisfactory results in the classification of seven emotions (happy, sad, angry, neutral, disgust, boredom and fear) but MLP classifier was the most prominent with an overall accuracy of 90.36%. A comparison between the performances of these classification algorithms is also presented.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Integration of Speech and Vision for Perception in Assistive Robots Using Robot Operating System
next chapter Plant Leaf Disease Detection Using Machine Learning Techniques
Literature
1.
Castellano G, Kessous L, Caridakis G (2008) Emotion recognition through multiple modalities: face, body gesture, speech. In: Affect and emotion in human-computer interaction. Springer, Berlin, Heidelberg, pp 92–103
2.
France DJ, Shiavi RG, Silverman S, Silverman M, Wilkes M (2000) Acoustical properties of speech as indicators of depression and suicidal risk. IEEE Trans Biomed Eng 47(7):829–837CrossRef
3.
Koolagudi SG, Rao KS (2012) Emotion recognition from speech: a review. Int J Speech Technol 15(2):99–117CrossRef
4.
Ververidis D, Kotropoulos C (2006) Emotional speech recognition: resources, features, and methods. Speech Commun 48(9):1162–1181CrossRef
5.
Burkhardt F, Paeschke A, Rolfes M, Sendlmeier WF, Weiss B (2005) A database of german emotional speech. In: Ninth european conference on speech communication and technology
6.
Ramdinmawii E, Mohanta A, Mittal VK (2017) Emotion recognition from speech signal. In: TENCON 2017–2017 IEEE region 10 conference. IEEE, Penang, Malaysia, pp 1562–1567
7.
Satt A, Rozenberg S, Hoory R (2017) Efficient emotion recognition from speech using deep learning on spectrograms. In: INTERSPEECH, pp 1089–1093
8.
Yenigalla P, Kumar A, Tripathi S, Singh C, Kar S, Vepa J (2018) Speech emotion recognition using spectrogram & phoneme embedding. In: INTERSPEECH, pp 3688–3692
9.
Badshah AM, Ahmad J, Rahim N, Baik SW (2017) Speech Emotion Recognition from Spectrograms with Deep Convolutional Neural Network. In: International conference on platform technology and service (PlatCon). IEEE, Busan, South Korea, pp 1–5
10.
Kaur J, Kumar A (2020) Databases, features and classification techniques for speech emotion recognition. Int J Innov Technol Exploring Eng 9(6):185–190CrossRef
11.
El Ayadi M, Kamel MS, Karray F (2011) Survey on speech emotion recognition: features, classification schemes, and databases. Pattern Recogn 44(3):572–587CrossRef
12.
Huang K, Wu C, Hong Q, Su M, Chen Y (2019) Speech emotion recognition using deep neural network considering verbal and nonverbal speech sounds. In: ICASSP 2019–2019 IEEE ınternational conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 5866–5870
13.
Sarker MK, Alam KMR, Arifuzzaman M (2014) Emotion recognition from speech based on relevant feature and majority voting. In: 2014 international conference on informatics, electronics & vision (ICIEV). IEEE, pp 1–5
14.
Chen H, Liu Z, Kang X, Nishide S, Ren F (2019) Investigating voice features for Speech emotion recognition based on four kinds of machine learning methods. In: 2019 IEEE 6th international conference on cloud computing and intelligence systems (CCIS). IEEE, pp 195–199
15.
Palo HK, Sagar S (2018) Comparison of neural network models for speech emotion recognition. In: 2018 2nd international conference on data science and business analytics (ICDSBA). IEEE, pp 127–131
Metadata
Title
Speech Emotion Recognition Using CNN, k-NN, MLP and Random Forest
Authors
Jasmeet Kaur
Anil Kumar
Copyright Year
2021
Publisher
Springer Nature Singapore
Book
Computer Networks and Inventive Communication Technologies

Print ISBN: 978-981-15-9646-9

Electronic ISBN: 978-981-15-9647-6

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-15-9647-6_39