Speech Quality Perception and its Estimation

语音质量的评估是包括电信、听力学和语音病理学在内的许多领域的重要课题。例如，语音质量测量被用于对电信中的语音编码、降噪和回声抑制算法的性能进行基准测试。* 同样，语音质量是用户接受助听器和 * 辅助听力设备并对其满意的重要因素。同样，语音质量测量在量化言语/声音障碍的严重程度和监测治疗进展方面是有用的。因此，良好的语音质量测量是一个 * 重要的参数，可以有效地评估通信和听力设备，以及 * 协助语音/声音障碍治疗管理。语音质量可以通过客观或主观的方法来评估。虽然语音质量的主观意见是最值得信赖的标准，但它们也很耗时和昂贵。* 因此，以合理的准确度预测主观语音质量得分的计算方法 * 很有吸引力，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。此外，我们还开发了一个神经模糊特征映射器和贝叶斯多元线性样条回归器 *，将这些特征向量映射到语音质量分数。我们的技术已成功地应用于电信，听力学和言语病理学领域的 * 问题。我们计划在未来五年内，通过研究更有效的特征提取和特征映射方案，通过将新的听力受损模型纳入助听器信号处理算法对感知语音质量的影响，以及通过开发新的仪器评估异常语音质量的方法，来建立我们的工作。

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.