Concatenative Speech Recognition using Morphemes

Abstract

This paper adopts a novel sub-lexical approach to construct viable continuous speech recognition systems with scalable vocabulary that use the components of words to form the elements of pronunciation dictionaries and recognition lattices. The proposed Concatenative ASR family utilizes combination rules between morphemes (prefixes, stems, and suffixes), along with their theoretical grammatical categories. The constrained structure reduces invalid words by using grammar rules governing agglutination of affixes with stems, while having a large vocabulary space and hence fewer out-of-vocabulary words. In pursuing this approach, the project develops automatic speech recognition (ASR) parameterized models, designs parameter values, constructs and implements ASR systems, and analyzes the characteristics of these systems. The project designs parameter values in the context of Arabic to yield a subset hierarchy of vocabularies of the ASR systems facilitating meaningful analysis. It investigates the characteristics of the ASR systems with respect to vocabulary, recognition lattice, dictionary, and word error rate (WER). In the experiments, the standard Word ASR model has the best characteristics for vocabulary of up to five thousand words and the Concatenative ASR family is most appropriate for vocabulary of up to half a million words. The paper shows that the approach used encompasses fundamentally different processes of word formation and thus is applicable to languages that exhibit concatenative word-formation processes.