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Low resource of parallel data is the key challenge of accent conversion(AC) problem in which both the pronunciation units and prosody pattern need to be converted. We propose a two-stage generative framework "convert-and-speak" in which the conversion is only operated on the semantic token level and the speech is synthesized conditioned on the converted semantic token with a speech generative model in target accent domain. The decoupling design enables the "speaking" module to use massive amount of target accent speech and relieves the parallel data required for the "conversion" module. Conversion with the bridge of semantic token also relieves the requirement for the data with text transcriptions and unlocks the usage of language pre-training technology to further efficiently reduce the need of parallel accent speech data. To reduce the complexity and latency of "speaking", a single-stage AR generative model is designed to achieve good quality as well as lower computation cost. Experiments on Indian-English to general American-English conversion show that the proposed framework achieves state-of-the-art performance in accent similarity, speech quality, and speaker maintenance with only 15 minutes of weakly parallel data which is not constrained to the same speaker. Extensive experimentation with diverse accent types suggests that this framework possesses a high degree of adaptability, making it readily scalable to accommodate other accents with low-resource data. Audio samples are available at https://www.microsoft.com/en-us/research/project/convert-and-speak-zero-shot-accent-conversion-with-minimumsupervision/., Comment: 9 pages, ACM MM2024(accepted)

Most existing masked audio modeling (MAM) methods learn audio representations by masking and reconstructing local spectrogram patches. However, the reconstruction loss mainly accounts for the signal-level quality of the reconstructed spectrogram and is still limited in extracting high-level audio semantics. In this paper, we propose to enhance the semantic modeling of MAM by distilling cross-modality knowledge from contrastive language-audio pretraining (CLAP) representations for both masked and unmasked regions (MAM-CLAP) and leveraging a multi-objective learning strategy with a supervised classification branch (SupMAM), thereby providing more semantic knowledge for MAM and enabling it to effectively learn global features from labels. Experiments show that our methods significantly improve the performance on multiple downstream tasks. Furthermore, by combining our MAM-CLAP with SupMAM, we can achieve new state-of-the-art results on various audio and speech classification tasks, exceeding previous self-supervised learning and supervised pretraining methods., Comment: Accepted by Interspeech2023