首个可实时处理的口音转换模型，保持原音色与语调。
Streaming Non-Autoregressive Model for Accent Conversion and Pronunciation Improvement
- 采用Emformer编码器与优化推理机制实现流式处理
- 在保留说话人特征和韵律的同时提升发音准确性
- 适合实时语音翻译、口语教学等场景
我们提出首个可流式处理的口音转换（AC）模型，能将非母语语音转换为接近母语口音，同时保持说话人身份、语调并改善发音。通过在原有AC架构中引入Emformer编码器和优化推理机制，实现了流式处理。此外，整合了母语文本到语音（TTS）模型以生成理想真值数据，提升训练效率。该流式AC模型性能媲美顶级非流式模型，且延迟稳定，是首个支持实时处理的口音转换系统。
原文摘要 · Abstract (English)
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.
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