Speech based emotion recognition using spectral feature extraction and an ensemble of kNN classifiers

Abstract

Security (and cyber security) is an important issue in existing and developing technology. It is imperative that cyber security go beyond password based systems to avoid criminal activities. A human biometric and emotion based recognition framework implemented in parallel can enable applications to access personal or public information securely. The focus of this paper is on the study of speech based emotion recognition using a pattern recognition paradigm with spectral feature extraction and an ensemble of k nearest neighbor (kNN) classifiers. The five spectral features are the linear predictive cepstrum (CEP), mel frequency cepstrum (MFCC), line spectral frequencies (LSF), adaptive component weighted cepstrum (ACW) and the post-filter cepstrum (PFL). The bagging algorithm is used to train the ensemble of kNNs. Fusion is implicitly accomplished by ensemble classification. The LDC emotional prosody speech database is used in all the experiments. Results show that the maximum gain in performance is achieved by using two kNNs as opposed to using a single kNN.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 47 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.