Integrating low-level speech features into a model of speech perception

为什么人类在识别语音方面比机器好得多？这项研究旨在衡量人类和机器在计算声音相似性方面的差异。语音感知的计算模型将在语音生成数据上进行训练，使用语音识别系统中通常使用的几种类型的特征。然后将测试它在语音识别任务中预测人类听者反应的能力。研究结果有望提供有关听者听到的语音如何影响他们对声音的感知的信息，以及自动语音识别系统使用的信息与人类听者使用的信息匹配程度的信息。通过让我们比较人类和语音识别系统在感知语音时如何使用信息，这项研究将提供一种工具，可以帮助使语音识别系统更像人类。逆向工程人类感知可以改善这些系统对新方言、说话者和噪音条件的概括方式。这有可能促进低资源语言系统的建设，扩大语音识别技术的影响。[支持SBE/BCS/PAC和CISE/IIS/RI]

Why are humans so much better than machines at recognizing speech? This research aims to measure differences between humans and machines in how they compute similarities between sounds. A computational model of speech perception will be trained on speech production data, using several types of features that are typically used in speech recognitions systems. It will then be tested on its ability to predict human listeners' responses in speech sound discrimination tasks. Results are expected to provide information about how the speech that listeners hear shapes their perception of sounds, as well as how well the information used by automatic speech recognition systems matches the information used by human listeners.By allowing us to compare how humans and speech recognition systems use information when perceiving speech, this research will provide a tool that can help make speech recognition systems more human-like. Reverse engineering human perception can improve the way these systems generalize to new dialects, talkers, and noise conditions. This has the potential to facilitate the construction of systems for low-resource languages, broadening the impact of speech recognition technologies.[Supported by SBE/BCS/PAC and CISE/IIS/RI]