在本論文中，我們提出以樹狀結構高斯密度為基礎的文字不特定語者調適系統。首先將一個訓練良好的通用背景模型以樹狀結構建造出在聲學空間中具有不同解析度的結構化背景模型，因此利用結構化的語者調適法調適出來的特定語者模型亦具有多重解析度的聲紋特性；利用樹狀結構於語者調適技術及語者模型在語者辨識正確率上有不錯的效果。
我們也將比較樹狀結構在不同語者調適方法上的效果。在少量調適語料的情況下，模型中沒有分到調適語料的高斯分佈會使得辨識的效能降低。因此對於沒有調適的高斯分佈，本論文提出一個加入結構化的向量場平滑化演算法，改善傳統向量場平滑化方法的缺點，進一步地提升系統的辨識效能。
在語者確認方面，樹狀結構中每一層解析度都有一定的效果，本論文也嘗試結合多層解析度計分的方法，以萃取在不同空間架構下的優缺點，以降低語者確認系統的等錯誤率。

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