Emotion Recognition From Speech Signals via a Probabilistic Echo-State Network

Abstract

The paper presents a probabilistic echo-state network (pi-ESN) for density estimation over variable-length sequences of multivariate random vectors. The pi-ESN stems from the combination of the reservoir of an ESN and a parametric density model based on radial basis functions. A constrained maximum likelihood training algorithm is introduced, suitable for sequence classification. Extensions of the algorithm to unsupervised clustering and semi-supervised learning (SSL) of sequences are proposed. Experiments in emotion recognition from speech signals are conducted on the WaSeP (C) dataset. Compared with established techniques, the pi-ESN yields the highest recognition accuracies, and shows interesting clustering and SSL capabilities

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Document Details

Document Type
Technical Report
Publication Date
Nov 15, 2015
Accession Number
AD1170911

Entities

People

  • Edmondo Trentin
  • Friedhelm Schwenker
  • Stefan Scherer

Organizations

  • Ulm University
  • University of Southern California

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence Software
  • Automata Theory
  • Bayes Theorem
  • Cognitive Systems Engineering
  • Computer Languages
  • Dimensionality Reduction
  • Hidden Markov Models
  • Information Processing
  • Information Science
  • Network Science
  • Neural Networks
  • Pattern Recognition
  • Probability
  • Recurrent Neural Networks
  • Reservoir Computing
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference