{"title":"利用自组织神经网络衍生的声学-语音特征识别音素","authors":"P. Dalsgaard, O. Andersen, R. Jørgensen","doi":"10.1109/NNSP.1992.253688","DOIUrl":null,"url":null,"abstract":"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.<<ETX>>","PeriodicalId":438250,"journal":{"name":"Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"On the identification of phonemes using acoustic-phonetic features derived by a self-organising neural network\",\"authors\":\"P. Dalsgaard, O. Andersen, R. Jørgensen\",\"doi\":\"10.1109/NNSP.1992.253688\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.<<ETX>>\",\"PeriodicalId\":438250,\"journal\":{\"name\":\"Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop\",\"volume\":\"30 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1992-08-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NNSP.1992.253688\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NNSP.1992.253688","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.<>