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Communication languages convey information through the use of a set of symbols or units. Typically, this unit is word. When developing language technologies, as words in a language do not have the same prior probability, there may not be sufficient training data for each word to model. Furthermore, the training data may not cover all possible words in the language. Due to these data sparsity and word unit coverage issues, language technologies employ modeling of subword units or subunits, which are based on prior linguistic knowledge. For instance, development of speech technologies such as automatic speech recognition system presume that there exists a phonetic dictionary or at least a writing system for the target language. Such knowledge is not available for all languages in the world. In that direction, this article develops a hidden Markov model-based abstract methodology to extract subword units given only pairwise comparison between utterances (or realizations of words in the mode of communication), i.e., whether two utterances correspond to the same word or not. We validate the proposed methodology through investigations on spoken language and sign language. In the case of spoken language, we demonstrate that the proposed methodology can lead up to discovery of phone set and development of phonetic dictionary. In the case of sign language, we demonstrate how hand movement information can be effectively modeled for sign language processing and synthesized back to gain insight about the derived subunits.

Subunits Inference and Lexicon Development Based on Pairwise Comparison of Utterances and Signs