人在溝通會把社交技能用在讓溝通更便捷的目的上,會展現一些特殊的
模式像是 accomodation。這些特殊的模式如果將它用 sensor 記錄下來,以
訊號的角度來看也能觀察到類似現象。甚至能更細微地看到人常會忽略
的地方。自閉症是一個被人稱做溝通與社交障礙的疾病,是一群被視為溝
通上有缺陷的人,至今為止我們都以醫療的角度來描述這個疾病,可能是
因為缺乏更細微的方式來定義自閉症導致到現在自閉症的定義都還不完
善,異質性高且診斷定義容易更換。因此這個研究想要以訊號的角度,透
過生成 speech analytics 來分析表達自閉症的 trait,我們首先從 non-verbal
的 analytics 來探討自閉症三個子類別之間的些微差異,我們接著藉由量化
speech 跟 language 去探討自閉症溝通能力上的幾個子向度的強弱差異,最
後我們藉由觀察 Vowel space 上的特性來探討自閉症與正常人的區別,以及
嚴重與輕微自閉症的差異。我們的研究在以上三個子課題都發現 insights,
這些 insights 提供了我們以另一種角度來看自閉症光譜的特殊行為特徵。
這篇論文的主要貢獻有兩個方面:語音算法的開發和語音分析所帶來的
洞見。在每個子主題中,我們都開發了語音算法來生成語音分析。除了對
自閉症特徵有更好的理解之外,這些語音分析還是數值,可以輸入到下游
的機器學習分類器中。這個特性使得在臨床場景中能夠進行自動評估成為
可能。因此,這些分析具有潛力被整合到邊緣設備中,以成為高效而可靠
的評估工具。此外,疾病進展追蹤和在生活中的自閉症風險篩查都是仍然
缺乏的潛在應用。至於從語音分析中獲得的洞見,它們提供了一個新的角
度來討論自閉症特徵。例如,我們發現了一個特殊的轉換結構適用於高功
能自閉症。因此,轉換互動的過程以及造成這一現象的原因都值得未來深入
探討。有了先進的工具和多角度來表徵自閉症特徵,我們可以更深入地
研究 ASD,並指定具體的行為表現形式以更好地理解。

In face-to-face communication, people have developed rich communication
skills to convey ideas and intentions for smoother interactions. Under this sce-
nario, specific patterns such as accommodation in speech tone or syntax can be
observed. It is possible to characterize these patterns by recording and analyzing
them. Moreover, from a signal perspective, the analytics characterizing human be-
havior can be beyond human imagination. For example, prosodic accommodation
manifests in the duration of each utterance, voice quality, and voice intensity.
Autism spectrum disorder (ASD) is a neural-developmental disorder featuring
communication, social reciprocity, and stereotyped behavior. The current defi-
nition of ASD is still vague, and diagnostic criteria change when versions of the
Diagnostic and Statistical Manual of Mental Disorders (DSM) are updated. Under-
standing the disease progression of autism is challenging because of its high het-
erogeneity. However, researchers have been studying ASD from a clinical angle.
ASD study from a signal-level point of view still falls short. With the advanced
technology that allows us to process and analyze signals from speech-language
sensors like microphones, it would be interesting to study autistic traits with com-
putational approaches.
Therefore, this study aims to analyze traits of ASD through signal processing,
utilizing speech analytics. In our first study, we explore subtle differences among
three subtypes of autism through non-verbal analytics. Then, we developed ver-
bal speech analytics to characterize speech and language involved in dialogue and
found their relationship with several psychological constructs about communica-tion deficits. Lastly, we investigate differences between autism and typical devel-
oping people, as well as differences between severe and mild cases of autism, by
observing characteristics in the vowel space.
There are two major contributions to this speech and language: speech algo-
rithm development and insights from speech analytics analyses. In each subtopic,
we develop speech algorithms to generate speech analytics. Besides a better under-
standing of autistic traits, these speech analytics are numerical values and can be
input to downstream machine learning classifiers. This property enables automatic
assessments in clinical scenarios. Hence, these analytics have the potential to be
integrated into edge devices to perform efficient and reliable assessment tools. In
addition, Disease progression tracking and into-life ASD risk screening are all pos-
sible applications that are still lacking. As for the insights from speech analytics
analyses, they provide a new point of view to discuss autistic traits. For exam-
ple, we found a special turn-taking structure for high-functioning autism. Hence,
how the turn-taking interaction goes and what causes this phenomenon is worthy
of future investigation. With advanced tools and multiple angles to characterize
autistic traits, we can push deeper into the studies of ASD and specify the concrete
behavioral manifestation for better understanding.

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