Department of Communication Disorders, Ariel University, Israel.
Amplio Learning Technologies, Rockville, MD.
J Speech Lang Hear Res. 2024 Sep 12;67(9):3004-3021. doi: 10.1044/2024_JSLHR-24-00009. Epub 2024 Aug 22.
Automatic speech analysis (ASA) and automatic speech recognition systems are increasingly being used in the treatment of speech sound disorders (SSDs). When utilized as a home practice tool or in the absence of the clinician, the ASA system has the potential to facilitate treatment gains. However, the feedback accuracy of such systems varies, a factor that may impact these gains. The current research analyzes the feedback accuracy of a novel ASA algorithm (Amplio Learning Technologies), in comparison to clinician judgments.
A total of 3,584 consonant stimuli, produced by 395 American English-speaking children and adolescents with SSDs (age range: 4-18 years), were analyzed with respect to automatic classification of the ASA algorithm, clinician-ASA agreement, and interclinician agreement. Further analysis of results as related to phoneme acquisition categories (early-, middle-, and late-acquired phonemes) was conducted.
Agreement between clinicians and ASA classification for sounds produced accurately was above 80% for all phonemes, with some variation based on phoneme acquisition category (early, middle, late). This variation was also noted for ASA classification into "acceptable," "unacceptable," and "unknown" (which means no determination of phoneme accuracy) categories, as well as interclinician agreement. Clinician-ASA agreement was reduced for misarticulated sounds.
The initial findings of Amplio's novel algorithm are promising for its potential use within the context of home practice, as it demonstrates high feedback accuracy for correctly produced sounds. Furthermore, complexity of sound influences consistency of perception, both by clinicians and by automated platforms, indicating variable performance of the ASA algorithm across phonemes. Taken together, the ASA algorithm may be effective in facilitating speech sound practice for children with SSDs, even in the absence of the clinician.
自动语音分析（ASA）和自动语音识别系统越来越多地应用于语音障碍（SSD）的治疗中。当作为家庭练习工具或在没有临床医生的情况下使用时，ASA 系统有可能促进治疗效果。然而，这些系统的反馈准确性存在差异，这可能会影响这些效果。本研究分析了一种新型 ASA 算法（Amplio Learning Technologies）的反馈准确性，将其与临床医生的判断进行比较。
共分析了 395 名患有 SSD 的美国英语儿童和青少年（年龄范围：4-18 岁）产生的 3584 个辅音刺激，以评估 ASA 算法的自动分类、临床医生与 ASA 的一致性以及临床医生之间的一致性。还进一步分析了结果与音位习得类别（早期、中期和晚期习得音位）的关系。
对于所有音位，临床医生和 ASA 分类之间对准确发音的一致性均高于 80%，但基于音位习得类别（早期、中期、晚期）存在一些差异。在将 ASA 分类为“可接受”、“不可接受”和“未知”（表示无法确定音位准确性）类别以及临床医生之间的一致性方面，也观察到了这种差异。对于发音错误的音位，临床医生与 ASA 的一致性降低。
Amplio 新型算法的初步研究结果表明，该算法在家庭练习的背景下具有潜在的应用价值，因为它对正确发音的声音具有较高的反馈准确性。此外，声音的复杂性会影响临床医生和自动平台的感知一致性，表明 ASA 算法在不同音位上的表现存在差异。总的来说，ASA 算法即使在没有临床医生的情况下，也可能有效地促进 SSD 儿童的语音练习。
