Some Issues Related to Phone Recognition and Language Identification Using Phonetic Engine

Abstract

The introduction of a phonetic engine in the literature is that it is a system which transforms a speech signal into some symbolic form. Phone recognition is a primary task of a PE. Among the various other applications of PE, one very useful application is the Language Identification (LID). This chapter discusses some issues pertaining to the use of PE in phone recognition as well as in language identification. Here, we have used PEs for three Indian Languages: Manipuri, Assamese and Bengali, in building the LID system. These languages are widely spoken in the Northeastern region of India. In the development of PEs, the International Phonetic Alphabet (IPA) symbols are used in the data transcription process. In modeling the phonetic units Hidden Markov Models (HMM) have been used in the training phase. These trained HMMs are then used in phone recognitions leading to the identification of language(s) of unknown test utterances. The overall phone recognition accuracies reported by the existing PEs for the above selected languages are $62.11\%$ for standard Manipuri language, $59.0\%$ for Kakching Dialect of Manipuri, $43.28\%$ for standard Assamese and $48.58\%$ for Bengali language. Automatic LID is possible using a set of PEs in testing unknown utterances due to the language bias of these systems. Various levels of identification rates reported in some LID tasks carried out with PEs are discussed here to look into the issues belonging to it.