由数据基础设施驱动的分层自动语音识别

A. Jagatheesan, Jong-Roon Ahnn, T. Phan, Abhishek Singh, Juhan Lee
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引用次数: 1

摘要

自动语音识别(ASR)多年来发展迅速,有望成为包括智能手机和可穿戴设备在内的移动设备的主要输入形式。如今,大多数大型移动平台都在云端执行语音识别。这种基于云的ASR (Cloud-ASR)方法既有优点也有缺点。云语音识别方法允许使用语音进行面向上下文的人机交互,而不仅仅是语音到文本的翻译。Cloud-ASR也有缺点,如没有接入Cloud-ASR时语音服务中断,以及无线电通信的能量消耗,这可能会更快地耗尽移动电池。我们建议使用分层语音识别器(HSR)作为替代方法来克服云- asr方法的缺点。在高铁方法中,移动设备尽可能自己执行“选择性语音识别”,而无需联系外部基于云的自动语音识别服务。在这个演示中,我们展示了我们的概念验证高铁及其可行性和优势。
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Hierarchical automatic speech recognition powered by data infrastructure
Automatic Speech Recognition (ASR) has evolved remarkably over the years and is expected to become a primary form of input to mobile devices including smartphones and wearables. Most large-scale mobile platforms perform speech recognition in the cloud today. There are both advantages and disadvantages to this Cloud-based ASR (Cloud-ASR) approach. Cloud-ASR approach allows for a context oriented humancomputer- interaction using speech rather than a mere speech-totext translation. A Cloud-ASR also has disadvantages such as interruption of the speech service when there is no access to the Cloud-ASR, and also the energy consumption for radio communications, which can drain a mobile battery sooner. We propose the usage of Hierarchical Speech Recognizer (HSR) as an alternative approach to overcome the shortcomings of the Cloud-ASR approach. In the HSR approach, mobile devices perform "selective speech recognition" by themselves as much as possible without contacting an external cloud-based ASR service. In this demonstration, we show our proof-of-concept HSR along with its feasibility and advantages.
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