Segmental phoneme recognition using piecewise linear regression

We propose an efficient, self-organizing segmental measurement based on piecewise linear regression (PLR) fit of the short-term measurement trajectories. The advantages of this description are: (i) it serves to decouple temporal measurements from the recognition strategy; and, (ii) it leads to lesser computation as compared with conventional methods. Also, acoustic context can be easily integrated into this framework. The PLR measurements are cast into a stochastic segmental framework for phoneme classification. We show that this requires static classifiers for each regression component. Finally, we evaluate this approach on the phoneme recognition task. Using the TIMIT database. This shows that the PLR description leads to a computationally simple alternative to existing approaches.<>