基于神经网络的语音合成
Speech Synthesis with Neural Networks
摘要：
文语转换传统上是通过连接语音的短样本或通过使用基于规则的系统将语音的语音表示转换成声学表示来执行的，声学表示然后被转换成语音。本文描述了一种系统，它使用一个时间延迟神经网络（TDNN）来执行这个语音到声学映射，与另一个神经网络来控制所生成的语音的定时。该神经网络系统比级联系统需要更少的内存，并且在与使用其他技术的商业系统进行比较的测试中表现良好。
英文摘要：
Text-to-speech conversion has traditionally been performed either by concatenating short samples of speech or by using rule-based systems to convert a phonetic representation of speech into an acoustic representation, which is then converted into speech. This paper describes a system that uses a time-delay neural network (TDNN) to perform this phonetic-to-acoustic mapping, with another neural network to control the timing of the generated speech. The neural network system requires less memory than a concatenation system, and performed well in tests comparing it to commercial systems using other technologies.
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。
