On hidden Markov models in isolated word recognition

Hidden Markov modeling has become an increasingly popular technique in automatic speech recognition. Recently, attention has been focused on the application of these models to talker-independent, isolated-word recognition. Initial results using models with discrete output densities for isolated-digit recognition were later improved using models based on continuous output densities. In a series of experiments on isolated-word recognition, we applied hidden Markov models with multivariate Gaussian output densities to the problem. Speech data was represented by feature vectors consisting of eight log area ratios and the log LPC error. A weak measure of vocal-tract dynamics was included in the observations by appending to the feature vector observed at time t, the vector observed at time t-δ, for some fixed offset δ. The best models were obtained with offsets of 75 or 90 msecs. When a comparison is made on a common data base, the resulting error rate of 0.2% for isolated-digit recognition improves on previous algorithms.