MRI: A Feature - Based Word Recognition System

摘要 该项目的目的是开发一种方法来实现一个功能- 的文字识别系统，可以科普广泛的 在声音表现中可能发生的变化， 话 单词的声学模式的可变性有很多 不同来源，包括说话者内部和说话者之间的差异 在说话风格和语速、各种语境效果以及 说话者之间的解剖学差异。 因此，尽管语言学 描述使用二进制值特征，声学相关 的特征具有不同程度的强度和范围， 时间 研究将分为三个阶段。 确保 对可能发生的可变性的充分表示， 将设计200字左右的数据库。 测试词 将从各种上下文中删除，并由几个 男性和女性都会说话 接下来，我们对 语料库将进行补充资料的文献， 特征的声学相关性和进一步的研究 可变性 最后，一个与说话者无关的孤立词 识别系统将开发和测试一个新的 同样的200字数据库。 认可制度将由三个部分组成： 用于提取声学特性的特性检测器， 特征，一个词汇，它将包括一些安排， 每个单词的特征，以及用于在 提取的声学模式和词汇表示。 有几种方法可以处理可变性。 一是 特征的声学特性将基于相对 这些措施将最大限度地减少对 演讲者的特点。 第二，不同于传统特征- 基于识别系统，将实现词汇通达 直接从声学特性，而不是从假设的 语音片段 通过这种方式，可以将上下文信息 在匹配过程中更直接地考虑。 最后，研究人员将开发一个表达框架， 词汇表征的变异性。 虽然 变异性有几个来源，似乎由此产生的 声学变化可以用几个特征来表示， 改变过程，包括语音弱化、特征 同化和其他一些结构依赖的变体。 因此，通过将改变特征的过程纳入 单词的词汇表示，它不应该是必要的， 识别系统已经“看到”或具有针对所有 一个特定的词在被翻译之前可能发生的变化， 能够识别它。 该项目的成果将有助于我们 对声学语音学的理解， 自动语音识别的改进技术。

ABSTRACT The aim of the project is to develop an approach to a feature- based word recognition system which can cope with the wide range of variability that can occur in the acoustic manifestation of words. The variability in the acoustic pattern of words has many different sources, including intra- and inter-speaker differences in speaking style and rate, various contextual effects, and anatomic differences between speakers. Thus, whereas linguistic descriptions use binary-valued features, the acoustic correlates of the feature have varying degrees of strength and extent over time. The research will be divided into three stages. To ensure an adequate representation of the variability that can occur, a database of around 200 words will be designed. The test words will be excised from a variety of contexts and spoken by several talkers, both males and females. Next, an acoustic study of the corpus will be conducted to supplement data in the literature on the acoustic correlates for features and to further study variability. Finally, a speaker-independent isolated word recognition system will be developed and tested with a new database of the same 200 words. The recognition system will consist of three components: property detectors for extracting the acoustic properties for features, a lexicon which will consist of some arrangement of features for each word, and a matcher for mapping between the extracted acoustic patterns and the lexical representation. There will be several ways to handle variability. First, the acoustic properties for features will be based on relative measures so that they will be minimally dependent upon the speaker's characteristics. Second, unlike traditional feature- based recognition systems, lexical access will be achieved directly from the acoustic properties rather than from postulated phonetic segments. In this way, contextual information can be taken into account more directly during the matching process. Finally, the investigator will develop a framework for expressing variability within the lexical representation of words. Although variability has several sources, it appears that the resulting acoustic changes can be expressed in terms of a few feature- altering processes including phonetic lenition, feature assimilation and some other structurally-dependent variants. Thus by incorporating the feature-altering processes in the lexical representation of words, it should not be necessary for the recognition system to have "seen" or have a rule for all of the variability that can occur in a particular word before it is able to recognize it. The results of this project should contribute to our understanding of acoustic phonetics, and also to the development of improved technologies for automatic speech recognition.