利用自组织神经网络衍生的声学-语音特征识别音素

P. Dalsgaard, O. Andersen, R. Jørgensen
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引用次数: 0

摘要

一个自组织神经网络(SONN)接受了由三个说话者连续说话的训练和校准过程。该过程的目的是建立一个能够将语音帧倒谱向量转换为连续值声音特征向量的系统。校准过程还包括一个阶段,在这个阶段,SONN的每个神经元被分配一个向量,定义语音技术和发音语音概念之间的联系。将语音测试语料库应用于SONN变换,验证了该变换方法的有效性。所建立的转换技术的主要结果以直方图的形式给出,通过直方图可以看出,计算的声学-语音特征值在很大程度上符合特征转换中使用的语音规范。直方图进一步用于证明声学-语音特征识别单个音素和区分声母和辅音音素的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On the identification of phonemes using acoustic-phonetic features derived by a self-organising neural network
A self-organizing neural network (SONN) is subjected to a training and calibration process using continuous speech spoken by three talkers. The aim of this process is to establish a system which is able to transform speech frame cepstrum vectors into vectors of continuous valued acoustic-phonetic features. The calibration process also involves a stage where each neuron of the SONN is assigned a vector defining the links between speech technology and articulatory phonetic concepts. The validity of the transformation approach is shown by applying a speech test corpus to the SONN transformation. The main results of the established transformation technique are given in a number of histograms by which it is shown that the computed acoustic-phonetic feature values to a large extent are in accordance with the phonological specifications used in the feature transformation. The histograms are further used to demonstrate the ability of the acoustic-phonetic features to identify individual phonemes and to discriminate between vocalic and consonantal phonemes.<>
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