增强众包音频以用于文本到语音模型
Enhancing Crowdsourced Audio for Text-to-Speech Models
AI总结：
本文针对众包音频质量不足的问题，在 Common Voice 加泰罗尼亚语子集上提出音频增强加 NISQA 自动过滤的去噪流水线，使扩散 TTS 模型的 UTMOS 分数提升 0.4。
摘要：
高质量音频数据是训练鲁棒文本到语音模型的关键前提，这往往限制了机会性数据集或众包数据集的使用。本文提出一种方法来克服这一限制，具体是在 Common Voice 的加泰罗尼亚语子集上实现一条去噪流水线；Common Voice 是一个以固有噪声和可变性著称的众包语料库。该流水线包含一个音频增强阶段，随后是选择性过滤策略。我们开发了一种自动过滤机制，利用非侵入式语音质量评估（NISQA）模型来识别并保留增强后质量最高的样本。为评估该方法的有效性，我们在处理后的数据集上训练了一个最先进的基于扩散的 TTS 模型。结果表明有显著改进，与未经增强的基线数据集相比，UTMOS 分数提高了 0.4。该方法显示出在 TTS 应用中扩大众包数据用途的前景，尤其适用于像加泰罗尼亚语这样的中低资源语言。
英文摘要：
High-quality audio data is a critical prerequisite for training robust text-to-speech models, which often limits the use of opportunistic or crowdsourced datasets. This paper presents an approach to overcome this limitation by implementing a denoising pipeline on the Catalan subset of Commonvoice, a crowd-sourced corpus known for its inherent noise and variability. The pipeline incorporates an audio enhancement phase followed by a selective filtering strategy. We developed an automatic filtering mechanism leveraging Non-Intrusive Speech Quality Assessment (NISQA) models to identify and retain the highest quality samples post-enhancement. To evaluate the efficacy of this approach, we trained a state of the art diffusion-based TTS model on the processed dataset. The results show a significant improvement, with an increase of 0.4 in the UTMOS Score compared to the baseline dataset without enhancement. This methodology shows promise for expanding the utility of crowdsourced data in TTS applications, particularly for mid to low resource languages like Catalan.
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。
