Automatic Phonetic Transcription of Non-Prompted Speech
Automatic Phonetic Transcription of Non-Prompted Speech
复制标题
无提示语音的自动语音转录
DOI：
10.5282/ubm/epub.13682
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发表时间：
1999
期刊：
影响因子：
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通讯作者：
F. Schiel
中科院分区：
文献类型：
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作者：
F. Schiel
“自动分段”（MAUS）系统以类似于训练有素的语音学家的方式对德语口语的语音成分进行标记和分段。MAUS已被用于训练自动语音识别（ASR）系统，以及提供详细的统计分析的自发语音（使用Verbmobil I和RVG I语料库）。MAUS系统是一个可靠的，自动化的手段来测试语言假设的语音特性的自发讲话，因此，应发挥重要作用，提供所需的经验数据，以开发更现实的口语模型。1.在许多情况下，我们的科学工作与记录非提示，甚至自发的德语在过去5年结束的结果，往往不同于我们的德语语音课本知识。根据这些观察，我认为，包括语音学在内的言语科学应该遵循一条新的道路（除了传统的道路，当然还需要追求！）要解决的问题是，言语的科学模型往往与现实存在显着差异。因此，在第二部分中，我将给出一些基于大型目的无关语音语料库的计算方法的论据。为了给这种类型的工作的一个例子，第三部分给出了一个简短的描述“慕尼黑自动分割”（MAUS）的方法，而最后一部分将给出三个例子，从MAUS的结果被用于不同的实验或应用。第一个例子是一个众所周知的同化过程在单词边界的统计评估;第二个和第三个例子描述的实验，以提高自动语音识别（ASR），通过利用知识的发音从MAUS分割。
Automatic Segmentation" (MAUS) system labels and segments the phonetic constituents of spoken German in a manner similar to highly trained phoneticians. MAUS has been used to train automatic speech recognition (ASR) systems as well as to provide detailed statistical analyses of spontaneous speech (using the Verbmobil I and RVG I corpora). The MAUS system is a reliable, automatic means of testing linguistic hypotheses concerning the phonetic properties of spontaneous speech and should therefore play an important role in providing the sort of empirical data required to develop more realistic models of spoken language. 1. INTRODUCTION In many cases our scientific work with recorded non− prompted or even spontaneous German during the last 5 years ended in results that often differ from our text book knowledge of German phonetics. In the light of these observations it is my opinion that the speech sciences including phonetics should follow a new way (beside the traditional ways that are of course still to be pursued!) to comply with the problem that often the scientific models of speech differ significantly from reality. Therefore, in part 2 I will give some arguments for computational methods on the basis of large purpose− independent speech corpora. To give an example of this type of work the third section gives a brief description of the 'Munich Automatic Segmentation' (MAUS) method, while the last part will give three examples where results from MAUS were used in different experiments or applications. The first example is a statistical evaluation of well known assimilation processes at word boundaries; the second and third example describe experiments to improve Automatic Speech Recognition (ASR) by exploiting the knowledge about pronunciation from the MAUS segmentation.