VanDam Mark, Oller D Kimbrough, Ambrose Sophie E, Gray Sharmistha, Richards Jeffrey A, Xu Dongxin, Gilkerson Jill, Silbert Noah H, Moeller Mary Pat
1Department of Speech & Hearing Sciences, Medical Sciences, Washington State University, Spokane, Washington, USA; 2School of Communication Sciences and Disorders and 3Institute for Intelligent Systems, University of Memphis, Memphis, Tennessee, USA; 4Konrad Lorenz Institute for Evolution and Cognition Research, Klosterneuburg, Austria; 5Center for Childhood Deafness, Boys Town National Research Hospital, Omaha, Nebraska, USA; 6Nuance Communications, Burlington, Massachusetts, USA; 7LENA Research Foundation, Boulder, Colorado, USA; and 8University of Cincinnati, Cincinnati, Ohio, USA.
Ear Hear. 2015 Jul-Aug;36(4):e146-52. doi: 10.1097/AUD.0000000000000138.
This study investigated automatic assessment of vocal development in children with hearing loss compared with children who are typically developing, have language delays, and have autism spectrum disorder. Statistical models are examined for performance in a classification model and to predict age within the four groups of children.
The vocal analysis system analyzed 1913 whole-day, naturalistic acoustic recordings from 273 toddlers and preschoolers comprising children who were typically developing, hard of hearing, language delayed, or autistic.
Samples from children who were hard of hearing patterned more similarly to those of typically developing children than to the language delayed or autistic samples. The statistical models were able to classify children from the four groups examined and estimate developmental age based on automated vocal analysis.
This work shows a broad similarity between children with hearing loss and typically developing children, although children with hearing loss show some delay in their production of speech. Automatic acoustic analysis can now be used to quantitatively compare vocal development in children with and without speech-related disorders. The work may serve to better distinguish among various developmental disorders and ultimately contribute to improved intervention.
本研究调查了与正常发育儿童、语言发育迟缓儿童和自闭症谱系障碍儿童相比,听力损失儿童的嗓音发育自动评估情况。对统计模型在分类模型中的性能以及预测这四组儿童年龄的能力进行了检验。
嗓音分析系统分析了273名学步儿童和学龄前儿童的1913份全天自然声学记录,这些儿童包括正常发育儿童、听力障碍儿童、语言发育迟缓儿童或自闭症儿童。
听力损失儿童的样本与正常发育儿童的样本模式更为相似,而与语言发育迟缓或自闭症儿童的样本不同。统计模型能够对所研究的四组儿童进行分类,并基于自动嗓音分析估计发育年龄。
这项研究表明,听力损失儿童与正常发育儿童之间存在广泛的相似性,尽管听力损失儿童在语音产生方面存在一些延迟。自动声学分析现在可用于定量比较有和没有与言语相关障碍的儿童的嗓音发育情况。这项工作可能有助于更好地区分各种发育障碍,并最终有助于改进干预措施。