在傳統語音辨識系統中，模型的訓練環境與測試環境不匹配(mismatch)是造成辨識率下降的首要問題，在此議題上，過去文獻已提出許多解決方法，如在語音模型端引入模型參數的不確定性所建立的強健性貝氏預測分類(Bayesian predictive classification)法則，或是調整模型於測試環境的調適方法，如最大事後機率(MAP)調適以及線性迴歸(MLLR)調適，甚至進一步考慮語音模型鑑別性之最小分類錯誤線性迴歸(MCELR)調適等方法。其中，貝氏預測分類法則是將模型參數的不確定性(uncertainty)適當的引入決策法則以達到決策方法的強健性，而參數不確定性反應了雜訊環境及聲學的變異性，它可由事前機率(prior density)來表示，傳統上貝氏學習提供了估測並更新參數事前資訊的機制。

為兼顧決策法則的強健性及鑑別性，本論文提出在貝氏預測分類架構下聲學模型及其事前機率模型之鑑別性訓練及更新，我們使用最小分類錯誤(MCE)之鑑別性準則來估測模型參數之超參數(hyperparameter)，並且提出了兩種更新的方法，其一是直接針對隱藏式馬可夫模型平均值向量參數更新其事前統計量；其二是考慮線性迴歸調整，針對迴歸矩陣之事前資訊在最小分類錯誤準則下做更新。在以AURORA 2雜訊語音資料庫為主的評估實驗中發現使用更新過後的事前機率可以提昇貝氏預測分類之鑑別性。

Robustness is a crucial issue for speech recognition because the mismatch between training and testing environments always exists in real-world applications. This mismatch problem will deteriorate system performance substantially. In the literature, many works have been proposed to deal with robustness issue. One is to consider the uncertainty of model parameters in acoustic modeling and fulfill the so-called Bayesian predictive classification (BPC). Alternatively, we can adapt acoustic model parameters to testing environments via distribution estimation approaches, e.g. maximum a posteriori (MAP) adaptation and maximum likelihood linear regression adaptation (MLLR) or discriminative estimation approaches, e.g. minimum classification error linear regression (MCELR). Discriminative approach estimates model parameters with larger separation and smaller classification errors. Basically, the consideration of model uncertainties is feasible to build robust decision rule. These uncertainties represent the variations of noise environment and acoustic signal.

To achieve decision robustness and discriminability, we develop the discriminative prior information for BPC decision rule. We utilize MCE criterion to estimate prior statistics to represent the randomness of system parameters. This discriminative prior estimation is realized both for direct adaptation framework which adapts the hyperparameters of hidden Markov model (HMM) parameters directly and indirect adaptation framework which adapts HMM parameters indirectly using linear regression matrices. In the experiments on noisy speech recognition, we find that discriminative BPC and linear regression BPC performs better than MAP, MLLR, MCELR adaptation and traditional BPC approach in presence of different noise conditions.

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