增强印尼语自动语音识别：评估具有多样语音变异性的多语言模型
Enhancing Indonesian Automatic Speech Recognition: Evaluating Multilingual Models with Diverse Speech Variabilities
AI总结：
本研究评估了MMS和Whisper等多语言模型在多样语音变异性下的印尼语ASR能力，发现微调的Whisper模型表现最佳，且说话风格变异性对模型性能影响最大。
摘要：
理想的语音识别模型能够在语音信号的各种特征下准确转录语音，如说话风格（朗读与自发）、语音语境（正式与非正式）以及背景噪声条件（干净与适中）。构建这样的模型需要大量具有多样语音特征的训练数据。目前，印尼语数据以朗读、正式和干净的语音为主，导致缺乏具有其他语音变异性的印尼语数据。为开发印尼语自动语音识别（ASR），我们研究了最先进的语音识别模型，即大规模多语言语音（MMS）和Whisper，并编制了一个包含具有变异性的印尼语语音数据集以促进研究。我们进一步调查了模型在不同变异性组下转录印尼语语音数据的预测能力。微调的Whisper模型在具有各种特征的数据集上取得了最佳结果，表现为词错误率（WER）和字符错误率（CER）的下降。此外，我们发现说话风格变异性对模型性能的影响最大。
英文摘要：
An ideal speech recognition model has the capability to transcribe speech accurately under various characteristics of speech signals, such as speaking style (read and spontaneous), speech context (formal and informal), and background noise conditions (clean and moderate). Building such a model requires a significant amount of training data with diverse speech characteristics. Currently, Indonesian data is dominated by read, formal, and clean speech, leading to a scarcity of Indonesian data with other speech variabilities. To develop Indonesian automatic speech recognition (ASR), we present our research on state-of-the-art speech recognition models, namely Massively Multilingual Speech (MMS) and Whisper, as well as compiling a dataset comprising Indonesian speech with variabilities to facilitate our study. We further investigate the models' predictive ability to transcribe Indonesian speech data across different variability groups. The best results were achieved by the Whisper fine-tuned model across datasets with various characteristics, as indicated by the decrease in word error rate (WER) and character error rate (CER). Moreover, we found that speaking style variability affected model performance the most.
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。
