论文标题
改善公共空间中社会机器人的强大ASR
Improved Robust ASR for Social Robots in Public Spaces
论文作者
论文摘要
部署在公共场所的社会机器人为ASR提出了一项具有挑战性的任务，因为各种因素，包括20至5 dB的噪声SNR。现有的ASR模型在此范围内对较高的SNR的表现良好，但噪音更大的降解。这项工作探讨了在这种情况下提供改善ASR性能的方法。我们使用Aishell-1中国语音语料库和Kaldi ASR工具包进行评估。我们能够超过SNR低于20 dB的最先进的ASR性能，这表明使用开源工具包实现相对较高的ASR和数百小时的培训数据的可行性，这通常可用。
Social robots deployed in public spaces present a challenging task for ASR because of a variety of factors, including noise SNR of 20 to 5 dB. Existing ASR models perform well for higher SNRs in this range, but degrade considerably with more noise. This work explores methods for providing improved ASR performance in such conditions. We use the AiShell-1 Chinese speech corpus and the Kaldi ASR toolkit for evaluations. We were able to exceed state-of-the-art ASR performance with SNR lower than 20 dB, demonstrating the feasibility of achieving relatively high performing ASR with open-source toolkits and hundreds of hours of training data, which is commonly available.
