Novel entropy based moving average refiners for HMM landmarks

The training of precise speech recognition models depends on accurate segmentation of the phonemes in a training corpus. Segmentation is typically performed using HMMs, but recent speech recognition work suggests that the transient acoustic features characteristic of manner-class phoneme boundaries (landmarks) may be more precisely localized using acoustic classifiers specifically designed for the task of landmark detection. This paper makes an empirical exploration of entropy based moving average techniques that are capable of improving the time alignment of phoneme boundaries proposed by an HMM-based speech recognizer. On a standard benchmark data set (A database of Hindi - National Language of India), we achieve new state-of-the-art performance, reducing RMS phone boundary alignment error from 28ms to 15ms.