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Automatic Emotion Recognition from Cochlear Implant-Like Spectrally Reduced Speech

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Ambient Assisted Living and Daily Activities (IWAAL 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8868))

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Abstract

In this paper we present a robust feature extractor that includes the In this paper we study the performance of emotion recognition from cochlear implant-like spectrally reduced speech (SRS) using the conventional Mel-frequency cepstral coefficients and a Gaussian mixture model (GMM)-based classifier. Cochlear-implant-like SRS of each utterance from the emotional speech corpus is obtained only from low-bandwidth subband temporal envelopes of the corresponding original utterance. The resulting utterances have less spectral information than the original utterances but contain the most relevant information for emotion recognition. The emotion classes are trained on the Mel-frequency cepstral coefficient (MFCC) features extracted from the SRS signals and classification is performed using MFCC features computed from the test SRS signals. In order to evaluate to the performance of the SRS-MFCC features, emotion recognition experiments are conducted on the FAU AIBO spontaneous emotion corpus. Conventional MFCC, Mel-warped DFT (discrete Fourier transform) spectrum-based cepstral coefficients (MWDCC), PLP (perceptual linear prediction), and amplitude modulation cepstral coefficient (AMCC) features extracted from the original signals are used for comparison purpose. Experimental results depict that the SRS-MFCC features outperformed all other features in terms of emotion recognition accuracy. Average relative improvements obtained over all baseline systems are 1.5% and 11.6% in terms of unweighted average recall and weighted average recall, respectively.

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Author information

Authors and Affiliations

  1. CRIM, Montreal, QC, Canada

    Md Jahangir Alam, Yazid Attabi & Patrick Kenny

  2. ETS, Montreal, QC, Canada

    Yazid Attabi & Pierre Dumouchel

  3. INRS-EMT, University of Quebec, Montreal, QC, Canada

    Douglas O’Shaughnessy

Authors
  1. Md Jahangir Alam
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  2. Yazid Attabi
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  3. Patrick Kenny
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  4. Pierre Dumouchel
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  5. Douglas O’Shaughnessy
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Editor information

Editors and Affiliations

  1. School of Engineering, University of Warwick, CV4 7AL, Coventry, UK

    Leandro Pecchia

  2. School of Computer Science and Informatics, De Montfort University, The Gateway, LE1 9BH, Leicester, UK

    Liming Luke Chen

  3. Institute, School of Computing and Mathematics, University of Ulster, Computer Science Research, Jordanstown Campus, Shore Road, BT37 0QB, Newtownabbey, UK

    Chris Nugent

  4. Escuela Superior de Informática, Castilla La Mancha University, Paseo de la Universidad 4, 13071, Ciudad, Real, Spain

    José Bravo

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© 2014 Springer International Publishing Switzerland

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Alam, M.J., Attabi, Y., Kenny, P., Dumouchel, P., O’Shaughnessy, D. (2014). Automatic Emotion Recognition from Cochlear Implant-Like Spectrally Reduced Speech. In: Pecchia, L., Chen, L.L., Nugent, C., Bravo, J. (eds) Ambient Assisted Living and Daily Activities. IWAAL 2014. Lecture Notes in Computer Science, vol 8868. Springer, Cham. https://doi.org/10.1007/978-3-319-13105-4_48

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  • DOI: https://doi.org/10.1007/978-3-319-13105-4_48

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13104-7

  • Online ISBN: 978-3-319-13105-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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