Streaming Non-Autoregressive Model for Accent Conversion and Pronunciation Improvement
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Abstract (arXiv)
We propose a first streaming accent conversion (AC) model that transforms non-native speech into a native-like accent while preserving speaker identity, prosody and improving pronunciation. Our approach enables stream processing by modifying a previous AC architecture with an Emformer encoder and an optimized inference mechanism. Additionally, we integrate a native text-to-speech (TTS) model to generate ideal ground-truth data for efficient training. Our streaming AC model achieves comparable performance to the top AC models while maintaining stable latency, making it the first AC system capable of streaming.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 23 of its 38 references that are in the map. First posted on arXiv 19 Jun 2025.
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23 of 38 references.
References · 23
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11 more
- 2020Relative Positional Encoding for Speech Recognition and Direct Translationcited by 23
- 2020Super-Human Performance in Online Low-latency Recognition of Conversational Speechcited by 20
- 2022Voice-preserving Zero-shot Multiple Accent Conversioncited by 17
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