基于音节分析的泰卢固语听写系统
Syllable Analysis to Build a Dictation System in Telugu language
摘要：
近几十年来，语音交互系统越来越受到人们的重视。为了开发像Dragon这样的印度语言听写系统，最重要的是使系统适应最少训练的说话者。在本文中，我们专注于创建语音数据库的重要性，在音节单位，并确定最小的文本要考虑，而训练任何语音识别系统。已经开发了用于英语和少数几种印度语言（如印地语和泰米尔语）的连续语音识别的系统。本文给出了泰卢固语音节的统计细节及其在语音识别过程中最小化搜索空间的应用。识别覆盖最大音节的最小单词。该词表可用于准备小文本，该小文本可用于在训练听写系统时收集语音样本。这些结果被绘制成音节频率和每个单词中的音节数。该方法应用于CIIL Mysore文本语料库，该语料库为300万字。
英文摘要：
In recent decades, Speech interactive systems gained increasing importance. To develop Dictation System like Dragon for Indian languages it is most important to adapt the system to a speaker with minimum training. In this paper we focus on the importance of creating speech database at syllable units and identifying minimum text to be considered while training any speech recognition system. There are systems developed for continuous speech recognition in English and in few Indian languages like Hindi and Tamil. This paper gives the statistical details of syllables in Telugu and its use in minimizing the search space during recognition of speech. The minimum words that cover maximum syllables are identified. This words list can be used for preparing a small text which can be used for collecting speech sample while training the dictation system. The results are plotted for frequency of syllables and the number of syllables in each word. This approach is applied on the CIIL Mysore text corpus which is of 3 million words.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
