Advances in phonetics-based sub-unit modeling for transcription alignment and sign language recognition
We explore novel directions for incorporating phonetic transcriptions into sub-unit based statistical models for sign language recognition. First, we employ a new symbolic processing approach for converting sign language annotations, based on HamNoSys symbols, into structured sequences of labels according to the Posture-Detention-Transition-Steady Shift phonetic model. Next, we exploit these labels, and their correspondence with visual features to construct phonetics-based statistical sub-unit models. We also align these sequences, via the statistical sub-unit construction and decoding, to the visual data to extract time boundary information that they would lack otherwise. The resulting phonetic sub-units offer new perspectives for sign language analysis, phonetic modeling, and automatic recognition. We evaluate this approach via sign language recognition experiments on an extended Lemmas Corpus of Greek Sign Language, which results not only in improved performance compared to pure data-driven approaches, but also in meaningful phonetic sub-unit models that can be further exploited in interdisciplinary sign language analysis.