scholarly journals Speaker Recognition Using Wavelet Cepstral Coefficient, I-Vector, and Cosine Distance Scoring and Its Application for Forensics

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Lei Lei ◽  
She Kun
Keyword(s):  
Speaker Recognition ◽  
Ambient Noise ◽  
Spectral Feature ◽  
Time Cost ◽  
Linear Discriminant ◽  
Recognition Model ◽  
The Troubles ◽  
Proposed Model ◽  
Robust Speaker Recognition ◽  
Cosine Distance

An important application of speaker recognition is forensics. However, the accuracy of speaker recognition in forensic cases often drops off rapidly because of the ill effect of ambient noise, variable channel, different duration of speech data, and so on. Therefore, finding a robust speaker recognition model is very important for forensics. This paper builds a new speaker recognition model based on wavelet cepstral coefficient (WCC), i-vector, and cosine distance scoring (CDS). This model firstly uses the WCC to transform the speech into spectral feature vecors and then uses those spectral feature vectors to train the i-vectors that represent the speeches having different durations. CDS is used to compare the i-vectors to give out the evidence. Moreover, linear discriminant analysis (LDA) and the within-class covariance normalization (WCNN) are added to the CDS algorithm to deal with the channel variability problem. Finally, the likelihood ratio estimates the strength of the evidence. We use the TIMIT database to evaluate the performance of the proposed model. The experimental results show that the proposed model can effectively solve the troubles of forensic scenario, but the time cost of the method is high.

scholarly journals Speaker Recognition Using Wavelet Packet Entropy, I-Vector, and Cosine Distance Scoring

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Lei Lei ◽  
She Kun
Keyword(s):  
Speaker Recognition ◽  
Wavelet Packet ◽  
Time Cost ◽  
Noisy Environment ◽  
Proposed Model ◽  
Speech Database ◽  
Recognition Result ◽  
Spectrum Feature ◽  
The Difference ◽  
Cosine Distance

Today, more and more people have benefited from the speaker recognition. However, the accuracy of speaker recognition often drops off rapidly because of the low-quality speech and noise. This paper proposed a new speaker recognition model based on wavelet packet entropy (WPE), i-vector, and cosine distance scoring (CDS). In the proposed model, WPE transforms the speeches into short-term spectrum feature vectors (short vectors) and resists the noise. I-vector is generated from those short vectors and characterizes speech to improve the recognition accuracy. CDS fast compares with the difference between two i-vectors to give out the recognition result. The proposed model is evaluated by TIMIT speech database. The results of the experiments show that the proposed model can obtain good performance in clear and noisy environment and be insensitive to the low-quality speech, but the time cost of the model is high. To reduce the time cost, the parallel computation is used.

scholarly journals Learning to Fool the Speaker Recognition

2021 ◽  
Vol 17 (3s) ◽  
pp. 1-21
Author(s):  
Jiguo Li ◽  
Xinfeng Zhang ◽  
Jizheng Xu ◽  
Siwei Ma ◽  
Wen Gao
Keyword(s):  
Success Rate ◽  
Speaker Recognition ◽  
State Of The Art ◽  
Low Frequency ◽  
Recognition System ◽  
Perceptual Quality ◽  
Recognition Model ◽  
Proposed Model ◽  
Adversarial Attack ◽  
Ablation Study

Due to the widespread deployment of fingerprint/face/speaker recognition systems, the risk in these systems, especially the adversarial attack, has drawn increasing attention in recent years. Previous researches mainly studied the adversarial attack to the vision-based systems, such as fingerprint and face recognition. While the attack for speech-based systems has not been well studied yet, although it has been widely used in our daily life. In this article, we attempt to fool the state-of-the-art speaker recognition model and present speaker recognition attacker , a lightweight multi-layer convolutional neural network to fool the well-trained state-of-the-art speaker recognition model by adding imperceptible perturbations onto the raw speech waveform. We find that the speaker recognition system is vulnerable to the adversarial attack, and achieve a high success rate on both the non-targeted attack and targeted attack. Besides, we present an effective method by leveraging a pretrained phoneme recognition model to optimize the speaker recognition attacker to obtain a tradeoff between the attack success rate and the perceptual quality. Experimental results on the TIMIT and LibriSpeech datasets demonstrate the effectiveness and efficiency of our proposed model. And the experiments for frequency analysis indicate that high-frequency attack is more effective than low-frequency attack, which is different from the conclusion drawn in previous image-based works. Additionally, the ablation study gives more insights into our model.

On the use of X-vectors for Robust Speaker Recognition

2018 ◽  
Author(s):  
Ondřej Novotný ◽  
Oldřich Plchot ◽  
Pavel Matějka ◽  
Ladislav Mošner ◽  
Ondřej Glembek
Keyword(s):  
Speaker Recognition ◽  
Robust Speaker Recognition

scholarly journals A Two-Stage Exon Recognition Model Based on Synergetic Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Zhehuang Huang ◽  
Yidong Chen
Keyword(s):  
Neural Network ◽  
Digital Signal ◽  
Two Stage ◽  
Recognition Model ◽  
Artificial Fish Swarm Algorithm ◽  
Proposed Model ◽  
Artificial Fish Swarm ◽  
Recognition Efficiency ◽  
Processing Techniques ◽  
Signal Processing Techniques

Exon recognition is a fundamental task in bioinformatics to identify the exons of DNA sequence. Currently, exon recognition algorithms based on digital signal processing techniques have been widely used. Unfortunately, these methods require many calculations, resulting in low recognition efficiency. In order to overcome this limitation, a two-stage exon recognition model is proposed and implemented in this paper. There are three main works. Firstly, we use synergetic neural network to rapidly determine initial exon intervals. Secondly, adaptive sliding window is used to accurately discriminate the final exon intervals. Finally, parameter optimization based on artificial fish swarm algorithm is used to determine different species thresholds and corresponding adjustment parameters of adaptive windows. Experimental results show that the proposed model has better performance for exon recognition and provides a practical solution and a promising future for other recognition tasks.

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