• Corpus ID: 11510797

20 Gender Classification in Emotional Speech

  title={20 Gender Classification in Emotional Speech},
  author={Mohammad Hossein Sedaaghi},


Documentation of the Sahand Emotional Speech database (SES)
  • Technical Report, Department of Electrical Eng., Sahand Univ. of Tech, Iran.
  • 2008
The Nature of Statistical Learning Theory
  • V. Vapnik
  • Computer Science
    Statistics for Engineering and Information Science
  • 2000
Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing
Probabilistic neural networks
  • D. Specht
  • Computer Science
    Neural Networks
  • 1990
Gender Classification in Emotional Speech
This research employs several classifiers and assesses their performance in gender classification by processing utterances from DES, where gender classification was incorporated in emotional speech recognition system using a wrapper approach based on back-propagation neural networks with sequential forward selection.
Comparison of Four Approaches to Age and Gender Recognition for Telephone Applications
  • Florian Metze, J. Ajmera, B. Littel
  • Computer Science
    2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07
  • 2007
A comparative study of four different approaches to automatic age and gender classification using seven classes on a telephony speech task and also compares the results with human performance on the same data.
Hierarchical Classification of Emotional Speech
This paper proposes in this paper some new harmonic and Zipf based features for better emotion class characterization and a hierarchical classification scheme as it was discovered that different emotional classes need different feature set for a better discrimination.
An overview of automatic speaker diarization systems
An overview of the approaches currently used in a key area of audio diarization, namely speaker diarizations, are provided and their relative merits and limitations are discussed.
Fast sequential floating forward selection applied to emotional speech features estimated on DES and SUSAS data collections
This paper classifies speech into several emotional states based on the statistical properties of prosody features estimated on utterances extracted from Danish Emotional Speech and a subset of Speech Under Simulated and Actual Stress data collections, demonstrating that gender and accent information reduce the classification error.
Robust GMM Based Gender Classification using Pitch and RASTA-PLP Parameters of Speech
The simulations show that the performance of the proposed gender classifier is excellent; it is very robust for noise and completely independent of languages; the classification accuracy is as high as above 98% for all clean speech and remains 95% for most noisy speech.
Scalable distributed speech recognition using Gaussian mixture model-based block quantisation