一种时间导数神经网络体系结构——时滞神经网络体系结构的替代方案

K. Paliwal
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引用次数: 1

摘要

虽然时滞神经网络结构最近在许多语音识别应用中得到了应用,但它存在不能使用较长时间上下文的问题,因为这增加了网络中连接权值的数量。这是一个严重的瓶颈,因为使用更大的时间上下文可以提高识别性能。本文提出了一种时间导数神经网络结构。这种体系结构的优点是,它可以利用有关较长时间上下文的信息,而不会增加网络中连接权重的数量。本文研究了该结构用于独立于说话人的孤立词识别,并将其性能与时滞神经网络结构进行了比较。结果表明,尽管时间导数神经网络结构使用较少的连接权值,但在语音识别方面仍优于时延神经网络结构
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A time-derivative neural net architecture-an alternative to the time-delay neural net architecture
Though the time-delay neural net architecture has been recently used in a number of speech recognition applications, it has the problem that it can not use longer temporal contexts because this increases the number of connection weights in the network. This is a serious bottleneck because the use of larger temporal contexts can improve the recognition performance. In this paper, a time-derivative neural net architecture is proposed. This architecture has the advantage that it can utilize information about longer temporal contexts without increasing the number of connection weights in the network. This architecture is studied here for speaker-independent isolated-word recognition and its performance is compared with that of the time-delay neural net architecture. It is shown that the time-derivative neural net architecture, in spite of using less number of connection weights, outperforms the time-delay neural net architecture for speech recognition.<>
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