傳統上大多數電腦輔助聲調學習的作法中會使用到一段預錄好的聲調樣版聲音作為比較的參考，並僅回饋數值式的分數作為聲調學習的回饋。在本篇論文中，我們提出了以決策樹為基礎之創新中文聲調評量技術，該技術使用了具聲調標記之平衡語料庫，並採用了在聲調發音上一連串的音調特徵，作為評量上的參考線索。藉由尋訪測試語句在決策樹中對應的路徑和節點，產生一連串具更正性的回應，該回應可帶給學習者更多可能的改進。此外，此技術可提供給學習者詳細的發音糾正指示以及不同發音路徑的比較結果，這是以分數為基礎的傳統電腦輔助聲調學習技術所無法提供的。

We propose a novel decision tree based approach to Mandarin tone assessment. In most conventional computer assisted pronunciation training (CAPT) scenarios, a tone production template is prepared as a reference with only numeric scores as feedbacks for tone learning. In contrast, decision trees trained with an annotated tone-balanced corpus make use of a collection of questions related to important cues in categories of tone production. By traversing the corresponding paths and nodes associated with a test utterance a sequence of corrective comments can be generated to guide the learner for potential improvement. Therefore, a detailed pronunciation indication or a comparison between two paths can be provided to learners, which are usually unavailable in score-based CAPT systems.