Speech and Music Quality: Perception and Prediction

语音和音乐质量：感知和预测

• 批准号: RGPIN-2021-04267
• 负责人: Parsa, Vijay
• 金额: $ 2.04万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749595
• 关键词: Speech; Music; Quality; Perception; Prediction

Speech quality assessment is an important research topic in Audiology and Speech Language Pathology (SLP). In the Audiology context, speech quality is an important factor in users' acceptance of and satisfaction with hearing aids (HAs) and assistive listening devices. As such, it is crucial to measure the impact of signal processing algorithms in hearing devices on speech quality. In the realm of SLP, speech quality measures are used to assess the severity of voice disorder and to monitor the progress over the course of voice therapy. Our interdisciplinary research applies engineering expertise to these two fields with the overarching goal of developing and evaluating novel clinical tools for speech quality assessment. Speech quality can be assessed through subjective or objective, instrumental means. In subjective speech quality assessment, normal or hearing impaired listeners are recruited and their perception of speech quality is recorded. While subjective opinions of speech quality have high degree of face validity, they are also time--consuming and resource intensive. Therefore, instrumental metrics derived from speech signals using computational models of audition are attractive and desirable. Our proposed research program builds on our expertise in objective speech quality modelling and encompasses lines of inquiry in both subjective and objective speech quality assessment. During the past few years, we have developed novel features for compact representation of speech quality, and several machine learning models to map these feature vectors into speech quality scores. We plan to build upon our work during the next five years by investigating more effective feature extraction and feature mapping schemes, and by incorporating new models of impaired hearing. In particular, within the Audiology theme, our goal is to develop and evaluate computational hearing and machine learning models for predicting the binaural sound quality and music quality perception by hearing impaired listeners. In a similar manner, we plan to develop and evaluate machine learning models for disordered speech quality perception in general, but more specifically the perception of speech produced by subjects with Parkinson's disease or subjects who have undergone laryngectomy. Our proposed work in the Audiology area will lead to the development of a clinical tool for speech and music quality assessment that Audiologists can use to independently verify manufacturers' claims about the benefits of their hearing aids. In particular, our proposed novel binaural metrics of sound quality will allow Audiologists to benchmark emerging binaural wireless hearing aids. Similarly, our proposed work on prediction of disordered speech quality ratings will lead to clinical tools that will increase efficiency in the assessment of abnormal voice quality, in prescribing a therapy protocol, and in monitoring the progress over the course of therapy.

语音质量评估是听力学和语音语言病理学的重要研究课题。在听力学背景下，语音质量是用户对助听器（HA）和辅助听力设备的接受和满意度的重要因素。因此，测量听力设备中的信号处理算法对语音质量的影响至关重要。在SLP领域，语音质量测量用于评估语音障碍的严重程度并监测语音治疗过程中的进展。我们的跨学科研究将工程专业知识应用于这两个领域，其总体目标是开发和评估用于语音质量评估的新型临床工具。语音质量可以通过主观或客观的工具手段进行评估。在主观语音质量评估中，招募正常或听力受损的听众，并记录他们对语音质量的感知。虽然语音质量的主观意见具有高度的表面效度，但它们也是耗时和资源密集的。因此，使用听觉的计算模型从语音信号导出的工具度量是有吸引力的和期望的。我们提出的研究计划建立在我们在客观语音质量建模方面的专业知识基础上，包括主观和客观语音质量评估方面的调查。在过去的几年中，我们已经开发了新的功能，用于语音质量的紧凑表示，以及几个机器学习模型，将这些特征向量映射到语音质量分数。我们计划在接下来的五年里，通过研究更有效的特征提取和特征映射方案，并结合新的听力受损模型，来建立我们的工作。特别是，在听力学主题中，我们的目标是开发和评估计算听力和机器学习模型，用于预测听力受损听众的双耳音质和音乐质量感知。以类似的方式，我们计划开发和评估机器学习模型，用于一般的语音质量感知障碍，但更具体地说，用于帕金森病受试者或接受过喉切除术的受试者产生的语音感知。我们在听力学领域的拟议工作将导致语音和音乐质量评估的临床工具的开发，听力学家可以使用该工具独立验证制造商关于其助听器益处的声明。特别是，我们提出的新的双耳音质指标将允许听力学家基准新兴的双耳无线助听器。类似地，我们提出的预测语音质量等级的工作将导致临床工具，这将提高评估异常语音质量，制定治疗方案和监测治疗过程中的进展的效率。