Recent advancements in Text-to-Speech (TTS) systems have enabled the generation of natural and expressive speech from textual input. Accented TTS aims to enhance user experience by making the synthesized speech more relatable to minority group listeners, and useful across various applications and context. Speech synthesis can further be made more flexible by allowing users to choose any combination of speaker identity and accent, resulting in a wide range of personalized speech outputs. Current models struggle to disentangle speaker and accent representation, making it difficult to accurately imitate different accents while maintaining the same speaker characteristics. We propose a novel approach to disentangle speaker and accent representations using multi-level variational autoencoders (ML-VAE) and vector quantization (VQ) to improve flexibility and enhance personalization in speech synthesis. Our proposed method addresses the challenge of effectively separating speaker and accent characteristics, enabling more fine-grained control over the synthesized speech. Code and speech samples are publicly available.

This paper proposes a end-to-end deep network to recognize kinds of accents under the same language, where we develop and transfer the deep architecture in speaker-recognition area to accent classification task for learning utterance-level accent representation. Compared with the individual-level feature in speaker-recognition, accent recognition throws a more challenging issue in acquiring compact group-level features for the speakers with the same accent, hence a good discriminative accent feature space is desired. Our deep framework adopts multitask-learning mechanism and mainly consists of three modules: a shared CNNs and RNNs based front-end encoder, a core accent recognition branch, and an auxiliary speech recognition branch, where we take speech spectrogram as input. More specifically, with the sequential descriptors learned from a shared encoder, the accent recognition branch first condenses all descriptors into an embedding vector, and then explores different discriminative loss functions which are popular in face recognition domain to enhance embedding discrimination. Additionally, due to the accent is a speaking-related timbre, adding speech recognition branch effectively curbs the over-fitting phenomenon in accent recognition during training. We show that our network without any data-augment preproccessings is significantly ahead of the baseline system on the accent classification track in the Accented English Speech Recognition Challenge 2020 (AESRC2020), where the state-of-the-art loss function Circle-Loss achieves the best discriminative optimization for accent representation.