Combining phonetic attributes using conditional random fields

Abstract

A Conditional Random Field is a mathematical model for sequences that is similar in many ways to a Hidden Markov Model, but is discriminative rather than generative in nature. In this paper, we explore the application of the CRF model to ASR processing of discriminative phonetic features by building a system that performs first-pass phonetic recognition using discriminatively trained phonetic features. With this system, we show that this CRF model achieves an accuracy level in a phone recognition task that is superior to a similarly trained HMM model. Index Terms: speech recognition, conditional random fields.