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- W3207018606 abstract "We propose an approach for cognitive coding of speech by unsupervised extraction of contextual representations in two hierarchical levels of abstraction. Speech attributes such as phoneme identity that last one hundred milliseconds or less are captured in the lower level of abstraction, while speech attributes such as speaker identity and emotion that persist up to one second are captured in the higher level of abstraction. This decomposition is achieved by a two-stage neural net-work, with a lower and an upper stage operating at different time scales. Both stages are trained to predict the content of the signal in their respective latent spaces. A top-down path-way between stages further improves the predictive capability of the network. With an application in speech compression in mind, we investigate the effect of dimensionality reduction and low bitrate quantization on the extracted representations. The performance measured on the LibriSpeech and EmoV-DB datasets reaches, and for some speech attributes even exceeds, that of state-of-the-art approaches." @default.
- W3207018606 created "2021-10-25" @default.
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- W3207018606 date "2022-05-23" @default.
- W3207018606 modified "2023-10-01" @default.
- W3207018606 title "Cognitive Coding Of Speech" @default.
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- W3207018606 doi "https://doi.org/10.1109/icassp43922.2022.9747914" @default.
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