受到全球化的影響，溝通時同時使用多國語言已經成為了一種必然趨勢。因為對多國語言的各種相關應用需求日益增加，多國語言的語音辨識器變得日益重要。在多國語言的語音辨識中，語句中夾帶的腔調會嚴重的影響辨識器之準確率。此外，語言轉換的現象也必然出現在多國語言的溝通之中，它也同樣的會大幅降低辨識器之效能。因此，具有語言轉換的語料庫也變得更重要。所以首先本論文著手建立中英雙語語言轉換語料庫。
為了去解決因為語言轉換所造成的語音辨識問題，語言轉換偵測就被應用來增進語言轉換語句的辨識效能。本論文使用一個基於隱含式語言空間模型(latent language space model)與差分貝氏資訊準則(delta-BIC)的方法去做語言轉換偵測。隱含式語言空間模型被用來描述各種語言，我們可對測試語言所建出的隱含式語言空間模型與各種語言的隱含式語言空間模型去計算相似度。計算相似度時會用到以歐氏距離為主與以餘弦角度為主之相似度計算來得到整體的相似度。
本論文也提出一個資料驅動的方法去建立中英文的語音辨識模型集。聲學特徵與具有跨語言特性的發音特徵被整合來計算三連音聲學模型的距離資訊。本文中的發音特徵是由深層學習網絡所擷取出來的，它可以被用來減輕前後文相關三連音模型經常要面對的資料稀疏問題。而語料中出現的三連音模型就可藉由聲學特徵與發音特徵來進行階層式聚類。
多語辨識經常得面對腔調相關問題。如何去模型化腔調變成是一種挑戰。本論文產生帶有中文腔調的英文模型以辨識母語為中文語者之語句。一重腔調語音片段驗證被用以自動擷取語料中之重腔調語音片段。再來，轉換函式與決策樹可根據無腔調的聲學模型去產生重腔調模型的高斯成份以解決資料稀疏之問題，最後再使用鑑別性函式檢測所產生出之重腔調模型之鑑別力,剔除鑑別力低之模型，所保留下來的重腔調模型將與正常發音模型一起對中英文的語句進行辨識。
此外，英文是最常被用於多語溝通的語言，然而因為母語的影響，不同地方的人在講英語都會有不同的腔調。本論文以發音腔調為基礎進行聚類，根據這樣的聚類結果，我們可挑選適合的腔調語音辨識器去進行腔調語音之辨識。本論文結合不變發音結構(invariant pronunciation structure)與支援向量回歸(support vector regression)去預測語者與語者之間發音的距離。
以上所有的方法都在本篇論文中被實作，且實驗結果顯示所提出的方法都能改進多語，語言轉換與腔調語音之辨識效能。

Due to globalization, multilingual communication is becoming more and more popular. With the increase of multilingual speech data and the need of wide applications, the development of multilingual automatic speech recognition (ASR) systems has become more and more important. For an ASR system, accents produced by non-native speakers will dramatically deteriorate the recognition performance. Moreover, Code-switching, a phenomenon of language change during conversation, could be easily found in multilingual communities. It also degrades the recognition accuracy of ASR seriously. Thus, the design and development of a code-switching speech database for ASR training becomes highly desirable. This dissertation presents the procedure for the design and development of a Chinese-English code-switching speech database.
In order to conquer the recognition problems caused by code-switching, code-switching event detection can be used to improve the recognition accuracy of ASR. This dissertation presents a new paradigm for code-switching event detection based on latent language space models (LLSMs) and delta-BIC. A LLSM is proposed to characterize a language by modeling the spatial relationships of the senones/articulatory features in the eigenspace using the PCA-transformed features. The language likelihood between the input speech LLSM and each of the language-dependent LLSMs is estimated basd on Euclidian distance-based and cosine angle distance-based similarities.
This dissertation also proposes a data-driven approach to phone set construction for code-switching ASR. Acoustic and context-dependent cross-lingual articulatory features (AFs) are incorporated into the estimation of the distance between triphone units for constructing a Chinese-English phone set. The AFs, extracted using a deep neural network, are used for code-switching articulation modeling to alleviate the data sparseness problem. The triphones are finally clustered to obtain a Chinese-English phone set.
Multilingual speech recognition is confronted with the accent-related problems caused by non-native speech. The acoustic properties in accented speech are quite divergent. The dissertation generates the highly Mandarin-accented English models for the speakers whose mother tongue is Mandarin. A verification method is proposed to extract the highly accented speech segments automatically. Gaussian components of the highly accented speech models are then generated from the corresponding Gaussian components of the native speech models using a linear transformation function and decision tree to deal with the data sparseness problem. A discrimination function is further applied to verify the generated accented acoustic models.
Furthermore, English is the most common language used by multiligual speakers. The dissertation creates a global pronunciation map of World Englishes. Successful clustering of accented English can be benifitial to speech recognition since people can choose suitable accented model to recognize. This dissertation investigates invariant pronunciation structure analysis and Support Vector Regression to predict the inter-speaker pronunciation distances for clustering.
These methods were implemented and the experimental results show that the proposed approaches achieved improvements in multilungual, code-switching and accented speech recognition.

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