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PHONEME BOUNDARY DETECTION USING LEARNABLE SEGMENTAL FEATURES

Phoneme boundary detection plays an essential first step for a variety of speech processing applications such as speaker diarization, speech science, keyword spotting, etc. In this work, we propose a neural architecture coupled with a parameterized structured loss function to learn segmental representations for the task of phoneme boundary detection. First, we evaluated our model when the spoken phonemes were not given as input.

Categories:
Other applications of machine learning (MLR-APPL)
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