Speech Quality Perception and its Estimation

语音质量的评估是包括电信、语音识别和语音识别在内的许多领域中的重要课题， 听力学和言语病理学。例如，语音质量测量用于对语音质量进行基准测试。 电信中的语音编码、降噪和回声抑制算法的性能。 类似地，语音质量是用户对助听器的接受度和满意度的重要因素， 助听器同样，语音质量测量在量化语音质量的严重性方面是有用的。 言语/声音障碍和监测治疗进展。因此，良好的语音质量度量是 这些重要参数将允许对通信和听力设备进行有效评估， 协助言语/声音障碍的治疗管理。 语音质量可以通过客观或主观的方法来评估。虽然主观的意见， 语音质量作为最值得信赖的标准是优选的，但是它们也是耗时且昂贵的。 因此，以合理的准确度预测主观语音质量分数的计算方法是 吸引人，并成为我们研究工作的重点。 在过去的几年中，我们已经开发了新的功能，紧凑的语音质量表示。在 此外，我们还开发了神经模糊特征映射器和贝叶斯多元线性样条回归器 以将这些特征向量映射到语音质量分数。我们的技术已成功应用于 电信、听力学和言语病理学领域的问题。我们计划在我们工作的基础上 在未来五年内，通过研究更有效的特征提取和特征映射方案， 在预测助听器信号处理算法对感知语音质量的影响时，结合新的听力受损模型，并开发新的异常语音质量仪器评估方法。

Assessment of speech quality is an important topic in a number of fields including telecommunications, Audiology, and Speech Pathology . For example, speech quality measurements are used to benchmark the performance of speech coding, noise reduction and echo suppression algorithms in telecommunications. Similarly, speech quality is an important factor in users' acceptance of and satisfaction with hearing aids and assistive listening devices. Likewise, speech quality measures are useful in quantifying the severity of a speech/voice disorder and in monitoring therapy progress. A good speech quality measure is therefore an important parameter that will allow efficient evaluation of communication and hearing devices, as well as assist in speech/voice disorder therapeutic management. Speech quality can be assessed through objective or subjective means. While subjective opinions of speech quality are preferred as the most trustworthy criteria, they are also time-consuming and expensive. Therefore, computational methods that predict subjective speech quality score with reasonable accuracy are attractive and form the focus of our research work. During the past few years, we have developed novel features for compact representation of speech quality. In addition, we have developed a neuro-fuzzy feature mapper and a Bayesian multivariate linear splines regressor to map these feature vectors into speech quality scores. Our techniques have been successfully applied to problems in telecommunications, Audiology, and Speech Pathology fields. We plan to build upon our work during the next five years by investigating more effective feature extraction and feature mapping schemes, by incorporating new models of impaired hearing in predicting the impact of hearing aid signal processing algorithms on perceived speech quality, and by developing new methodologies for instrumental assessment of abnormal voice quality.