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首页> 外文期刊>International Journal of Computer Processing of Oriental Languages >Experimental Studies Using Statistical Algorithms on Transliterating Phoneme Sequences for English-Chinese Name Translation
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Experimental Studies Using Statistical Algorithms on Transliterating Phoneme Sequences for English-Chinese Name Translation

机译：用统计算法对音名序列进行音译的实验研究

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摘要

Machine transliteration is automatic generation of the phonetic equivalents in a target language given a source language term, which is useful in many cross language applications. Transliteration between far distant languages, e.g. English and Chinese, is challenging because their phonological dissimilarities are significant. Existing techniques are typically rule-based or statistically noisy channel-based. Their accuracies are very low due to their intrinsic limitations on modeling transcription details. We propose direct statistical approaches on transliterating phoneme sequences for English-Chinese name translation. Aiming to improve performance, we propose two direct models: First, we adopt Finite State Automata on a process of direct mapping from English phonemes to a set of rudimentary Chinese phonetic symbols plus mapping units dynamically discovered from training. An effective algorithm for aligning phoneme chunks is proposed. Second, contextual features of each phoneme are taken into consideration by means of Maximum Entropy formalism, and the model is further refined with the precise alignment scheme using phoneme chunks. We compare our approaches with the noisy channel baseline that applies IBM SMT model, and demonstrate their superiority.
机译：机器音译是在给定源语言术语的情况下自动生成目标语言中的语音等效项，这在许多跨语言应用程序中很有用。遥远语言之间的音译，例如英文和中文具有挑战性，因为它们的语音差异非常明显。现有技术通常基于规则或基于统计噪声通道。由于它们在模拟转录细节方面的固有局限性，因此其准确性非常低。我们提出了直接统计方法对音译序列进行音译，以进行英汉姓名翻译。为了提高性能，我们提出了两种直接模型：首先，在从英语音素到一组基本汉语注音符号的直接​​映射以及从训练中动态发现的映射单元的过程中，采用有限状态自动机。提出了一种有效的音素组对齐算法。其次，通过最大熵形式主义考虑每个音素的上下文特征，并使用音素块使用精确的对齐方案进一步完善该模型。我们将我们的方法与应用IBM SMT模型的嘈杂的渠道基准进行了比较，并证明了它们的优越性。

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