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Performance Estimation of Noisy Speech Recognition Considering Recognition Task Complexity

机译:考虑识别任务复杂度的噪声语音识别性能估计

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To ensure a satisfactory QoE (Quality of Experience) and facilitate system design in speech recognition services, it is essential to establish a method that can be used to efficiently investigate recognition performance in different noise environments. Previously, we proposed a performance estimation method using a spectral distortion measure. However, there is the problem that recognition task complexity affects the relationship between the recognition performance and the distortion value. To solve this problem, this paper proposes a novel performance estimation method considering the recognition task complexity. We confirmed that the proposed method gives accurate estimates of the recognition performance for various recognition tasks by an experiment using noisy speech data recorded in a real room.
机译:为了确保令人满意的QoE(体验质量)并促进语音识别服务中的系统设计,必须建立一种可用于有效研究不同噪声环境中的识别性能的方法。以前,我们提出了一种使用频谱失真度量的性能估计方法。然而,存在识别任务复杂度影响识别性能与失真值之间的关系的问题。为了解决这个问题,本文提出了一种新的考虑识别任务复杂度的性能估计方法。我们确认,通过使用记录在真实房间中的嘈杂语音数据进行的实验,提出的方法可以准确估计各种识别任务的识别性能。

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