Unsupervised phonetic and word level discovery for speech to speech translation for unwritten languages

We experiment with unsupervised methods for deriving and clustering symbolic representations of speech, working towards speech-to-speech translation for languages without regular (or any) written representations. We consider five low-resource African languages, and we produce three different segmental representations of text data for comparisons against four different segmental representations derived solely from acoustic data for each language. The text and speech data for each language comes from the CMU Wilderness dataset introduced in [1], where speakers read a version of the New Testament in their language. Our goal is to evaluate the translation performance not only of acoustically derived units but also of discovered sequences or "words" made from these units, with the intuition that such representations will encode more meaning than phones alone. We train statistical machine translation models for each representation and evaluate their outputs on the basis of BLEU-1 scores to determine their efficacy. Our experiments produce encouraging results: as we cluster our atomic phonetic representations into more word-like units, the amount information retained generally approaches that of the actual words themselves.