SPEECH ENHANCEMENT USING LONG SHORT-TERM MEMORY BASED RECURRENT NEURAL NETWORKS FOR NOISE ROBUST SPEAKER VERIFICATION

被引：0
|
作者：
Kolbaek, Morten [1 ]
Tan, Zheng-Hua [1 ]
Jensen, Jesper [1 ]
机构：
[1] Aalborg Univ, Dept Elect Syst, Fredrik Bajers Vej 7, DK-9220 Aalborg, Denmark
来源：
2016 IEEE WORKSHOP ON SPOKEN LANGUAGE TECHNOLOGY (SLT 2016) | 2016年
基金：
欧盟地平线“2020”;
关键词：
Speaker Verification; Long Short-Term Memory; Deep Neural Networks; Speech Enhancement; RSR2015; RECOGNITION; MODELS;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper we propose to use a state-of-the-art Deep Recurrent Neural Network (DRNN) based Speech Enhancement (SE) algorithm for noise robust Speaker Verification (SV). Specifically, we study the performance of an i-vector based SV system, when tested in noisy conditions using a DRNN based SE front-end utilizing a Long Short-Term Memory (LSTM) architecture. We make comparisons to systems using a Non-negative Matrix Factorization (NMF) based front-end, and a Short-Time Spectral Amplitude Minimum Mean Square Error (STSA-MMSE) based front-end, respectively. We show in simulation experiments that a male-speaker and text-independent DRNN based SE front-end, without specific a priori knowledge about the noise type outperforms a text, noise type and speaker dependent NMF based front-end as well as a STSA-MMSE based front-end in terms of Equal Error Rates for a large range of noise types and signal to noise ratios on the RSR2015 speech corpus.
引用
收藏
页码：305 / 311
页数：7
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