通过文本转语音与语音转换增强语音识别
Enhancing Speech Recognition through Text-to-Speech and Voice Conversion Augmentation
AI总结：
本研究针对呼叫中心-银行领域，对比TTS与KNNVC、Seed-VC等语音转换方法，发现其可显著提升ASR的语音质量与准确率，为ASR在行业场景的应用提供指导。
摘要：
鉴于获取大量真实音频存在挑战，自动语音识别（ASR）系统的发展日益依赖合成数据。本研究聚焦呼叫中心-银行领域，针对该场景下的ASR模型开展针对性分析。我们评估真实与合成数据集，以词错误率（WER）和字符错误率（CER）为指标，采用语音质量模型评估其对ASR性能的影响。本研究对比文本转语音（TTS）与先进语音转换方法，包括KNNVC、Seed-VC及Vec2Wav2，发现这些方法可显著提升语音质量与ASR准确率，其中Seed-VC的效果尤为突出。此外，我们的领域特定实验揭示了将ASR技术应用于行业相关场景时面临的独特挑战与机遇，凸显语音转换技术增强ASR系统的潜力，为不同语言场景的未来研究提供指导，推动ASR创新在各领域的广泛应用。
英文摘要：
Given the challenges associated with obtaining large volumes of real-world audio, the advancement of Automatic Speech Recognition (ASR) systems increasingly depends on synthetic data. This study focuses on the call center-banking domain, providing a targeted analysis of ASR models in this context. We evaluate real and synthetic datasets, using Word Error Rate (WER) and Character Error Rate (CER) as metrics, and employ a speech quality model to assess the impact on ASR performance. Our research compares Text-to-Speech (TTS) and advanced voice conversion methods, including KNNVC, Seed-VC, and Vec2Wav2, revealing significant improvements in speech quality and ASR accuracy, particularly with Seed-VC. Additionally, our domain-specific experimentation provides insight into the unique challenges and opportunities that arise when applying ASR technologies to industry-relevant settings. This highlights the potential of voice conversion technologies to enhance ASR systems, guiding future research in diverse linguistic scenarios, and paving the way for the broader application of ASR innovations across various fields.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
