标题： 通过优化合成数据生成提高语音识别性能 显示英文标题

标题： Towards Improved Speech Recognition through Optimized Synthetic Data Generation

摘要： 监督训练语音识别模型需要访问转录的音频数据，这通常由于保密问题而无法实现。 我们解决这个问题的方法是使用具有语音克隆功能的最先进的文本到语音模型，从仅文本语料库生成合成音频。 我们的目标是实现与在真实数据上训练的模型相当的自动语音识别（ASR）性能。 我们探索通过微调、过滤和评估来优化合成数据生成的方法，并将其用于训练端到端编码器-解码器ASR模型。 实验使用了两个魁北克法语的自发对话语音数据集。 我们表明，改进数据生成可以显著提高在合成数据上训练的最终ASR系统的性能。 显示英文摘要

摘要： Supervised training of speech recognition models requires access to transcribed audio data, which often is not possible due to confidentiality issues. Our approach to this problem is to generate synthetic audio from a text-only corpus using a state-of-the-art text-to-speech model with voice cloning capabilities. Our goal is to achieve automatic speech recognition (ASR) performance comparable to models trained on real data. We explore ways to optimize synthetic data generation through finetuning, filtering and evaluation, and its use for training an end-to-end encoder-decoder ASR model. Experiments were conducted using two datasets of spontaneous, conversational speech in Qu\'ebec French. We show that improving data generation leads to large improvements in the final ASR system trained on synthetic data.