臺灣博碩士論文加值系統

統計式n-gram模型是目前最普遍的語言模型之一，然而，長距離資訊的缺乏、資料稀疏和訓練與測試環境不匹配問題都將嚴重的影響n-gram語言模型的效能。由於n-gram模型的假設，當字詞間的距離超過定義的視窗大小時，其間的相依性將被忽略，因此n-gram將難以長距離的語言資訊。又由於訓練語料的不足，資料稀疏問題導致未出現的字機率為零，n-gram模型的一般化能力(Generalization)將被限制。這個問題當考慮長距離的資料或高階n-gram時將會更加嚴重。此論文著眼於建立一連續型語言模型表示法以改善語言模型所面臨的問題。傳統n-gram在離散的辭彙空間中，是利用詞彙比對求得對應機率，因此訓練語料中沒出現過的事件將會無對應之機率，導致機率為零。反之，我們利用潛在主題式資訊將離散詞彙空間轉換到低維度之連續型空間，詞彙序列將利用主題事後機率表示成一連續向量，並利用最小平方法估出最佳投影矩陣，進而在此空間中計算其語言模型機率，此時沒出現字詞將可藉由訓練語料中相似的字辭估算其最佳機率，進而達到解決資料稀疏的問題，在此連續主題空間中也將可更有效的對模型參數加以調整。又由於主題模型的建立，也將能增加模型對長距離資訊的擷取的能力。此論文也將針對n-gram模型所面臨的三大問題，探討相關文獻中提出之解決方法與此連續主題語言模型的關聯性。在實驗中，我們實作此新穎語言模型方法於華爾街日報(Wall Street Journal)大辭彙連續語音料庫，並對語言模型複雜度與語音辨識錯誤率加以分析。

Statistical n-gram language models suffer form the three weakness which are capacity of long distance information, data sparse and domain mismatch. This study presents the continuous topic language model to improve the robustness of probability prediction. Continuous representation of word sequence can effectively solve data sparseness problem in n-gram language model, where the discrete variables of words are represented and the unseen events are prone to happen. This problem is increasingly severe when extracting long-distance regularities for high-order n-gram model. Rather than considering discrete word space, we construct the continuous space to represent word sequence where the latent topic information is embedded. The continuous vector is formed by the topic posterior probabilities .The least squares projection matrix from discrete word space to continuous topic space is accordingly estimated. The unseen words can be predicted through the new continuous representation of language model. Also, performing language model adaptation in continuous topic space can increase the robustness of the model. Word distribution of an unseen history in the adaptation data can be estimated through considering the neighboring histories in the continuous topic space. Also, using topic framework makes it feasible to exploit long distance regularities. In the experiments, we implement the proposed method on using Wall Street Journal corpus and obtain the significant performance improvement over the conventional latent topic language model.

中文摘要: i
Abstract: ii
章節目次 iii
圖目錄 v
表目錄 vi
第一章 緒論 1
第二章 語音辨識簡介 5
2.1 語音辨識概論 5
2.2 語言模型 7
2.3 語言模型之評估 13
2.4 N-gram模型之缺點 13
第三章 N-gram語言模型改進方向 15
3.1 長距離語言模型 15
3.2 語言模型平滑化方法 21
3.2.1 回退式平滑化 (Backoff Smoothing) 22
3.2.2 類別語言模型(Class-based language models) 24
3.2.3 連續空間語言模型 24
第四章 語言模型之連續型辭彙表示法 28
4.1 連續辭彙表示法 28
4.1.1 潛在主題語言模型 30
4.1.2 連續空間之建立 31
4.1.3 階層式連續主題空間 32
4.2 長距離資訊擷取之討論 35
4.3 資料稀疏問題之討論 36
4.4 語言模型調整之討論 37
第五章 實驗 40
5.1 語料庫簡介 40
5.2 實驗設定 42
5.3 實驗結果 44
5.3.1 潛在主題模型之分析 44
5.3.2 Perplexity分析 47
5.3.3 語音辨識效能分析 50
5.4 系統展示 53
第六章 結論與未來研究方向 57
參考文獻 59

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