Accent Conversion

Accent conversion (AC) aims to transform the accent of speech while maintaining speaker identity and semantic content. Current research focuses on developing models that disentangle segmental and prosodic aspects of speech, leveraging techniques like variational autoencoders, generative adversarial networks, and sequence-to-sequence models, often trained on synthetic or minimally supervised data to address data scarcity issues. These advancements hold significant potential for improving speech recognition, enhancing cross-cultural communication, and creating more inclusive speech technologies.