Generated Speech Classification Using Convolution, Quantization, and BERT

The patent implements a feedback mechanism by training a classifier model on a dataset containing both real and generated speech samples. The classifier provides feedback signals that enable the system to distinguish between authentic and synthesized speech, thereby mitigating the harmful effects of advanced speech generation technologies while preserving their beneficial naturalness characteristics.

The patent segments the speech classification task into distinct functional components: a feature extractor that processes raw speech data through one-dimensional convolution operations, a quantizer that converts feature vectors into discrete representations, and a classifier model that performs the final authentication. This segmentation allows each component to be optimized independently, managing overall system complexity while maintaining high classification accuracy.

The patent applies residual vector quantization by transforming continuous feature vectors into discrete quantized representations through a learned codebook. This parameter transformation efficiently compresses the feature space while preserving essential speech characteristics, reducing information loss. The residual nature of the quantization allows iterative refinement, improving representation efficiency despite the increased processing complexity of the quantization operation.