scholarly journals GuidedMix: An on‐the‐fly data augmentation approach for robust speaker recognition system

2021 ◽  
Author(s):  
Runqiu Xiao ◽  
Zhuo Li ◽  
Xiaoxiao Miao ◽  
Wenchao Wang ◽  
Pengyuan Zhang
Keyword(s):  
Speaker Recognition ◽  
Data Augmentation ◽  
Recognition System ◽  
Robust Speaker Recognition

scholarly journals A robust speaker recognition based on Data augmentation

2020 ◽  
Keyword(s):  
Speaker Recognition ◽  
Data Augmentation ◽  
Robust Speaker Recognition

Abstract The authors have requested that this preprint be withdrawn due to erroneous posting.

scholarly journals A robust speaker recognition based on Data augmentation

2020 ◽  
Author(s):  
Chenqi Li ◽  
Haoyuan Lu ◽  
Wei Wang
Keyword(s):  
Performance Improvement ◽  
Speaker Recognition ◽  
Large Scale ◽  
Data Augmentation ◽  
Environmental Noise ◽  
Experimental Results ◽  
Training Data ◽  
Robust Speaker Recognition

Abstract It is known that large-scale training data can get the better effect of recognition. However, it is difficult to collect a lot of labeled training data for speaker recognition. At the same time, the performance of speaker recognition is greatly influenced by environmental noise. In this paper, we use data augmentation by adding noise to get much training data and improve the robustness of speaker recognition. The experimental results demonstrate that data augmentation have the better performance improvement on Chinese-863 database.

scholarly journals Cost-Sensitive Learning for Emotion Robust Speaker Recognition

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Dongdong Li ◽  
Yingchun Yang ◽  
Weihui Dai
Keyword(s):  
Speaker Recognition ◽  
Recognition System ◽  
Learning Technology ◽  
Speech Corpus ◽  
Voice Communication ◽  
Cost Sensitive Learning ◽  
Identification Rate ◽  
Telephone System ◽  
Robust Speaker Recognition ◽  
Voice Data

In the field of information security, voice is one of the most important parts in biometrics. Especially, with the development of voice communication through the Internet or telephone system, huge voice data resources are accessed. In speaker recognition, voiceprint can be applied as the unique password for the user to prove his/her identity. However, speech with various emotions can cause an unacceptably high error rate and aggravate the performance of speaker recognition system. This paper deals with this problem by introducing a cost-sensitive learning technology to reweight the probability of test affective utterances in the pitch envelop level, which can enhance the robustness in emotion-dependent speaker recognition effectively. Based on that technology, a new architecture of recognition system as well as its components is proposed in this paper. The experiment conducted on the Mandarin Affective Speech Corpus shows that an improvement of 8% identification rate over the traditional speaker recognition is achieved.

A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Recognition System ◽  
Learning Approach ◽  
Arabic Text ◽  
Data Set ◽  
Processing Step ◽  
Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

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