Low-resource automatic speech recognition (ASR) is challenging due to a scarcity of annotated data. While synthetic data from text-to-speech (TTS) systems can augment ASR training, its efficacy for low-resource languages remains unclear. In this study, we investigate under which conditions TTS-based data augmentation is most effective for low-resource languages. Experiments on six low-resource languages in Common Voice show that synthetic data is most beneficial under extremely low-resource ASR conditions (i.e., less than one hour of available real speech data), or for languages with larger amounts of TTS data (i.e., more than 10 hours). Additionally, increasing the amount and diversity of synthetic data while keeping an appropriate ratio of synthetic-to-real data can further improve ASR performance.

“基于信息论的言语产出研究发现携带信息量越大的语言单位,其语音信号越容易被强化。目前的相关研究主要通过自信息的方式衡量语言单位信息量,但该方法难以对长距离的上下文语境进行建模。本研究引入基于预训练语言模型GPT-2和文本-拼音互信息的语言单位信息量衡量方式,考察汉语的单词、韵母和声调信息量对语音产出的韵律特征的影响。研究结果显示汉语中单词和韵母信息量更大时,其韵律特征倾向于被增强,证明了我们提出的方法是有效的。其中信息量效应在音长特征上相比音高和音强特征更显著。”