Robust speech-based happiness recognition

Chang Hong Lin, Ernestasia Siahaan, Yu Hau Chin, Bo Wei Chen, Jia Ching Wang, Jhing Fa Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


This paper presents a robust happiness recognition system. The system consists of a happiness recognition module and a noise suppression module. In the happiness recognition module, we present an emotion feature set comprising Mel-frequency cepstral coefficients (MFCCs), the subband powers, spectral centroid, spectral spread, spectral flatness, RSS, pitch and energy. The proposed feature set is fed into a probability product support vector machine for happiness recognition. In real world applications, the speech received are often exposed to noise, thus prone to reducing the recognition rate. We propose a noise suppression method using subspace based method. A gain function estimation method is used for time domain constrained (TDC) based subspace speech enhancement. The optimal Lagrange multiplier of the gain function will be estimated in accordance with signal to noise ratio (SNR) of the noisy speech. The proposed happiness recognition system has been tested using a large number of noisy speech utterances with a 34% equal error rate.

Original languageEnglish
Title of host publicationICOT 2013 - 1st International Conference on Orange Technologies
Number of pages4
StatePublished - 2013
Event1st International Conference on Orange Technologies, ICOT 2013 - Tainan, Taiwan
Duration: 12 Mar 201316 Mar 2013

Publication series

NameICOT 2013 - 1st International Conference on Orange Technologies


Conference1st International Conference on Orange Technologies, ICOT 2013


  • Happiness recognition
  • emotional speech
  • noise suppression
  • probability product kernel


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