Cognitive Coding of Speech
This work addresses speech compression by improving representation extraction for attributes like phonemes and speaker identity, but it appears incremental as it builds on existing neural network methods.
The paper tackles the problem of cognitive coding of speech by proposing a two-stage neural network that extracts hierarchical contextual representations at different time scales, achieving performance that reaches or exceeds state-of-the-art approaches on LibriSpeech and EmoV-DB datasets.
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 network, 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 pathway 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.