Intimidate
发表于 2025-3-21 16:40:17
书目名称Computational Processing of the Portuguese Language影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0232923<br><br> <br><br>书目名称Computational Processing of the Portuguese Language影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0232923<br><br> <br><br>书目名称Computational Processing of the Portuguese Language网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0232923<br><br> <br><br>书目名称Computational Processing of the Portuguese Language网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0232923<br><br> <br><br>书目名称Computational Processing of the Portuguese Language被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0232923<br><br> <br><br>书目名称Computational Processing of the Portuguese Language被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0232923<br><br> <br><br>书目名称Computational Processing of the Portuguese Language年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0232923<br><br> <br><br>书目名称Computational Processing of the Portuguese Language年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0232923<br><br> <br><br>书目名称Computational Processing of the Portuguese Language读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0232923<br><br> <br><br>书目名称Computational Processing of the Portuguese Language读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0232923<br><br> <br><br>
evince
发表于 2025-3-21 22:38:27
Computational Processing of the Portuguese Language6th International Wo
取之不竭
发表于 2025-3-22 01:42:42
Nuno J. Mamede,Isabel Trancoso,Maria Graças Volpe
阻挠
发表于 2025-3-22 07:46:47
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Gustatory
发表于 2025-3-22 09:48:05
Devoicing Measures of European Portuguese Fricativestio of variances of the laryngograph signal during the VF transition and during the fricative) were used to classify the examples into two or three categories. The results of the automatic and manual measures of devoicing are compared, and an explanation for observed misclassifications is presented.
embolus
发表于 2025-3-22 13:35:34
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embolus
发表于 2025-3-22 19:34:53
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悦耳
发表于 2025-3-23 00:43:26
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打包
发表于 2025-3-23 01:32:56
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fatty-acids
发表于 2025-3-23 06:54:16
Evaluation of a Segmental Durations Model for TTSn. This model is based on artificial neural networks. The evaluation of the model quality was made by comparison with read speech. The standard deviation reached in test set is 19.5 ms and the linear correlation coefficient is 0.84. The model is perceptually evaluated with 4.12 against 4.30 for natu