Combining Frame and Segment Level Processing via Temporal Pooling for Phonetic Classification
نویسندگان
چکیده
We propose a simple, yet novel, multi-layer model for the problem of phonetic classification. Our model combines the frame level transformation of the acoustic signal with the segment level transformation via a temporal pooling architecture to compute class conditional probabilities of phones. Without the use of any phonetic knowledge, our model achieved the state-ofthe-art performance on the TIMIT phone classification task. The flexibility of our model allows us to mix a variety of pooling architectures, leading to further significant performance improvements.
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