Testing the ability of speech recognizers to measure the effectiveness of encoding algorithms for digital speech transmission

C. Chernick, S. Leigh, K. L. Mills, R. Toense
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引用次数: 18

Abstract

Modern communication channels, such as digital cellular telephony, often convey human speech in a highly encoded form. Methods that rely on human subjects to evaluate the quality of such channels are too costly to deploy on a large scale; thus, automated methods are often used to model quality as perceived by humans. Traditional automated methods that use signal to noise ratios (SNR) to judge the quality of channels do not model human perception well when applied to highly encoded speech. For this reason, researchers investigate alternative means to objectively measure the quality of such channels. We explore the feasibility and applicability of using automated speech recognition technology to model human perception of the quality of communication channels that carry highly encoded (compressed) human speech.
测试语音识别器的能力,以衡量数字语音传输编码算法的有效性
现代通信渠道,如数字蜂窝式电话,经常以高度编码的形式传达人类语言。依靠人类受试者来评估这些渠道的质量的方法成本太高,无法大规模部署;因此,自动化的方法经常被用来为人类感知的质量建模。传统的自动化方法使用信噪比(SNR)来判断信道的质量,当应用于高度编码的语音时,不能很好地模拟人类的感知。出于这个原因,研究人员研究了客观衡量这些频道质量的替代方法。我们探讨了使用自动语音识别技术来模拟人类对承载高度编码(压缩)人类语音的通信通道质量的感知的可行性和适用性。
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