基于LSTM神经网络的说话人识别系统构建

Computational Science and Techniques Pub Date : 2018-06-22 DOI:10.15181/CSAT.V6I1.1579

Laurynas Dovydaitis, V. Rudzionis

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引用次数: 4

摘要

本文利用长短期记忆深度神经网络对立陶宛语母语使用者的识别结果进行了分析。我们着眼于识别的准确性并确定进一步的改进潜力。用于训练和说话人识别的数据集由370多个独特的说话人组成，他们用立陶宛语提供他们的语音。在本文中，我们给出了来自该数据集的部分结果。DOI: 10.15181 / csat.v6i1.1579

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Building LSTM Neural Network Based Speaker Identification System

In this paper, we are analyzing the results of native Lithuanian speaker recognition and identification using long short-term memory deep neural network. We look at recognition accuracy and identify further potential improvements. Dataset used for training and speaker recognition consists of over 370 unique speakers, who provide their voice utterances in Lithuanian language. In this paper we present results that are derived from part of this dataset. DOI: 10.15181/csat.v6i1.1579

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Computational Science and Techniques

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