Expanding articulatory information from ultrasound imaging of speech using MRI-based image simulations and audio measurements

使用基于 MRI 的图像模拟和音频测量来扩展语音超声成像的发音信息

• 批准号: 10537976
• 负责人: Sarah Rotong Li
• 金额: $ 3.97万
• 依托单位: UNIVERSITY OF CINCINNATI
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10537976
• 关键词: 3-Dimensional; Acoustics; Address; Adult; Affect; Age; Air; Area; Articulation; Biofeedback Training; Black race; Characteristics; Child; Client; Clinical; Computer software; Data; Data Collection; Data Set; Disease; Education; Employment; Feedback; Fellowship; Future; Geometry; Goals; Guidelines; Hand; Health; Image; Image Analysis; Individual; Institution; Interpersonal Relations; Intervention; Knowledge; Language; Language Development; Learning; Magnetic Resonance Imaging; Measurement; Methods; Modeling; Morphologic artifacts; Movement; Neural Network Simulation; Obstructive Sleep Apnea; Oryctolagus cuniculus; Outcome; Palate; Pathologist; Production; Recommendation; Research; Research Personnel; Rotation; Schools; Shapes; Source; Speech; Speech Disorders; Speech Sound; Speech Therapy; Structure; Stuttering; Surface; Techniques; Technology; Testing; Time; Tissue Model; Tissue imaging; Tissues; Tongue; Tooth structure; Training; Translational Research; Ultrasonic wave; Ultrasonography; United States; Universities; Variant; Visit; Visual; Work; base; deep learning; deep learning model; imaging Segmentation; imaging system; improved; insight; machine learning model; neural network; novel; novel strategies; predictive modeling; prevent; simulation; skills; social; sound; speech accuracy; success; tissue mapping; tongue apex; tool; ultrasound

PROJECT SUMMARY Ultrasound imaging provides articulatory feedback useful for remediating speech sound disorders, which affect 5% of children and cause long-term deficits in social health and employment in adulthood. However, ultrasound imaging can be difficult to interpret for clinicians and individuals, limiting the understanding of articulatory data and ultrasound biofeedback therapy speech outcomes. A likely source of difficulty is the articulatory information missing from ultrasound images, such as the tongue tip and reference vocal tract structures (e.g., palate) that cannot be consistently imaged with ultrasound due to air. Much of this missing information from ultrasound can be ascertained in magnetic resonance imaging (MRI) because MRI images the entire vocal tract. Comparing ultrasound images and MRI will improve interpretation of ultrasound images by confirming that certain characteristics of ultrasound images (e.g., obscured tongue tip, double edge artifacts) occur from characteristics of tongue shapes; as well, models can be trained to predict from ultrasound images the articulatory information shown in MRI. However, articulatory variability prevents direct comparison between these images. A novel approach to avoid variability is to simulate ultrasound wave propagation in tissue segmented from MRI. Recent advancements in deep learning have also demonstrated ability to address the inverse problem of predicting articulation from acoustic data. Thus, to meet the needs of improving ultrasound image interpretation, the goal for this proposal is to use simulated ultrasound images and neural network models to characterize and predict articulatory information missing from 2D midsagittal ultrasound images. These models will be trained on MRI and audio data. We will characterize missing articulatory information by developing efficient simulation of ultrasound images from MRI tissue segmentation. One hypothesis that will be tested is the guideline for using the lower edge of double edge artifacts in ultrasound images as the tongue surface. To test this guideline for a greater range of data (including disordered child speakers and different simulated probe rotations), double edge artifacts will be compared with tissue maps used to generate the simulated images. Another comparison will estimate the amount of tongue tip typically missing in /r/ tongue shapes. We will then develop a deep learning model that trains on information from MRI to predict midsagittal vocal tract shapes (including the tongue tip and palate) from the inputs of tongue contours from ultrasound and audio. With these aims, we will add insight to ultrasound imaging for speech and provide a tool with future applications in expanding articulatory information, e.g., testing outcomes of using more complete vocal tract information in ultrasound biofeedback therapy. Training for this fellowship will occur at the University of Cincinnati, with opportunities to visit labs at two additional institutions. The proposed plan provides training from a range of investigators in topics such as ultrasound imaging and application to speech research, developing skills needed for my future goals.

项目摘要 超声成像提供了可用于治疗言语声音障碍的发音反馈， 这影响了5%的儿童，并导致成年后社会健康和就业的长期缺陷。 然而，超声成像可能难以为临床医生和个人解释，限制了超声成像的应用。 了解发音数据和超声生物反馈治疗语音结果。的可能来源 难点在于超声图像中缺少发音信息，例如舌尖和参考 声道结构（例如，腭），其由于空气而不能用超声一致地成像。 超声中缺失的大部分信息可以在磁共振成像中确定 (MRI)因为核磁共振成像能显示整个声道比较超声图像和MRI将改善 通过确认超声图像的某些特征（例如， 模糊的舌尖，双边缘伪影）从舌头形状的特征中出现;同样，模型可以 被训练以从超声图像预测MRI中所示的发音信息。然而，发音 可变性阻止了这些图像之间的直接比较。一种避免可变性的新方法是 模拟从MRI分割组织中的超声波传播。深度学习的最新进展 也证明了解决从声学数据预测清晰度的逆问题的能力。 因此，为了满足改善超声图像解释的需要，该提议的目标是使用 模拟超声图像和神经网络模型来表征和预测发音信息 2D正中矢状超声图像中缺失。这些模型将在MRI和音频数据上进行训练。 我们将通过开发有效的超声模拟来表征缺失的发音信息 MRI组织分割图像。将被检验的一个假设是使用较低 超声图像中的双边缘伪影边缘作为舌面。为了测试这一准则， 数据范围（包括无序儿童扬声器和不同的模拟探头旋转），双边 将伪影与用于生成模拟图像的组织图进行比较。另一个比较将 估计/r/舌形中通常缺失的舌尖的量。然后我们将开发一种深度学习 该模型训练来自MRI的信息，以预测中矢状声道形状（包括舌尖和 腭）从来自超声和音频的舌头轮廓的输入。有了这些目标，我们将增加洞察力， 用于语音的超声成像并且提供了一种在扩展发音信息中具有未来应用的工具， 例如，在一个实施例中，测试在超声生物反馈治疗中使用更完整的声道信息的结果。 该奖学金的培训将在辛辛那提大学进行，并有机会在两点参观实验室。 更多机构。拟议的计划提供了一系列调查人员的培训，主题包括 超声波成像和应用到语音研究，发展我未来目标所需的技能。