Speech Recognition using Acoustic Landmarks and Binary Phonetic Feature Classifiers

In spite of decades of research, Automatic Speech Recognition (ASR) is far from reaching the goal of performance close to Human Speech Recognition (HSR). One of the reasons for unsatisfactory performance of the state-of-the-art ASR systems, that are based largely on Hidden Markov Models (HMMs), is the inferior acoustic modeling of low level or phonetic level linguistic information in the speech signal. An acoustic-phonetic approach to ASR, on the other hand, explicitly targets linguistic information in the speech signal. But an acoustic phonetic system that carries out large ASR speech recognition tasks, for example, connected word or continuous speech recognition, does not exist. We propose a probabilistic and statistical framework for ASR based on the knowledge of acoustic phonetics for connected word ASR. The proposed system is based on the idea of representation of speech sounds by bundles of binary valued articulatory phonetic features. The probabilistic framework requires only binary classifiers of phonetic features and the knowledge based acoustic correlates of the features for the purpose of connected word speech recognition. We explore the use of Support Vector Machines (SVMs) for binary phonetic feature classification because of the favorable properties well suited to our recognition task that SVMs offer. In the proposed method, probabilistic segmentation of speech is obtained using SVM based classifiers of manner phonetic features. The linguistically motivated landmarks obtained in each segmentation is used for classification of source and place phonetic features. Probabilistic segmentation paths are constrained using Finite State Automata (FSA) for isolated or connected word recognition. The proposed method could overcome the disadvantages encountered by the early acoustic-phonetic knowledge based systems, that led the ASR community to switch to ASR systems highly dependent on statistical pattern analysis methods.