Speech Quality Perception and its Estimation

语音质量评估是包括电信在内的多个领域中的重要主题， 听力学和言语病理学。例如，语音质量测量被用来对 电信中语音编码、降噪和回声抑制算法的性能。 同样，语音质量是用户对助听器和助听器接受和满意的重要因素 辅助听音设备。同样，语音质量测量在量化严重程度时也很有用 语言/声音障碍和监测治疗进展。因此，一个好的语音质量衡量标准是 重要参数，可有效评估通信和听力设备以及 协助语言/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进行评估。而主观上的观点 语音质量被认为是最可信的标准，但它们也很耗时和昂贵。 因此，以合理的精度预测主观语音质量分数的计算方法是 并成为我们研究工作的重点。 在过去的几年里，我们开发了新的特征来紧凑地表示语音质量。在……里面 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 将这些特征向量映射到语音质量分数。我们的技术已经成功地应用于 电信、听力学和语言病理学领域的问题。我们计划在我们工作的基础上再接再厉 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方面纳入听力受损的新模型，并开发用于异常语音质量的仪器评估的新方法。

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.