JaCHMM: A Java-based conditioned Hidden Markov Model library

Abstract

We present JaCHMM, a Java implementation of a conditioned Hidden Markov Model (CHMM), which is made available under BSD license. It is based on the open source library “Jahmm” and provides implementations of the Viterbi, Forward-Backward, Baum-Welch and K-Means algorithms, all adapted for the CHMM. Like the Hidden Markov Model (HMM), the CHMM may be applied to a wide range of uni- and multimodal classification problems. The library is intended for academic and scientific purposes but may be also used in commercial systems. As a proof of concept, the JaCHMM library is successfully applied to speech-based emotion recognition outperforming HMM- and SVM-based approaches.