scholarly journals Blind score normalization method for PLDA based speaker recognition

2015 ◽  
Author(s):  
Danila Doroshin ◽  
Nikolay Lubimov ◽  
Marina Nastasenko ◽  
Mikhail Kotov
Keyword(s):  
Speaker Recognition ◽  
Normalization Method ◽  
Score Normalization

Analysis of Score Normalization in Multilingual Speaker Recognition

2017 ◽  
Author(s):  
Pavel Matějka ◽  
Ondřej Novotný ◽  
Oldřich Plchot ◽  
Lukáš Burget ◽  
Mireia Diez Sánchez ◽  
...  
Keyword(s):  
Speaker Recognition ◽  
Score Normalization

The Non-Intellectual Norm of Middle School Students’ Mathematics Learning and Its Grade Evaluation Standard: Taking Tianjin as an Example

2021 ◽  
Vol 7 (1) ◽  
pp. 907-922
Author(s):  
Guangming Wang ◽  
Jian Li ◽  
Jingxian Jian
Keyword(s):  
Middle School ◽  
Middle School Students ◽  
Applied Research ◽  
Mathematics Learning ◽  
Middle School Student ◽  
Normalization Method ◽  
School Students ◽  
Evaluation Standard ◽  
Score Normalization

Using the "Middle School Student Mathematics Learning Non-intellectual Questionnaire," a total of 1,400 middle school students in 11 districts and counties of Tianjin were surveyed. According to the data, using the raw score normalization method and the formula “T = 50+10×Z”, the middle school student mathematics learning non-intellectual population and its sub-dimensions norm table were established the corresponding grade evaluation standard was determined. The results of applied research were analyzed for class and individual application cases, and corresponding suggestions were made based on the analysis results.

Distant Speaker Recognition: An Overview

2016 ◽  
Vol 13 (02) ◽  
pp. 1550032 ◽  
Author(s):  
Mohammad Ali Nematollahi ◽  
S. A. R. Al-Haddad
Keyword(s):  
Speaker Recognition ◽  
Ambient Noise ◽  
Specific Information ◽  
Viable Systems ◽  
Speech Distortion ◽  
Score Normalization ◽  
Robust Feature Extraction ◽  
Feature Normalization ◽  
Performance Results ◽  
Real Time Application

Distant speaker recognition (DSR) system assumes the microphones are far away from the speaker’s mouth. Also, the position of microphones can vary. Furthermore, various challenges and limitation in terms of coloration, ambient noise and reverberation can bring some difficulties for recognition of the speaker. Although, applying speech enhancement techniques can attenuate speech distortion components, it may remove speaker-specific information and increase the processing time in real-time application. Currently, many efforts have been investigated to develop DSR for commercial viable systems. In this paper, state-of-the-art techniques in DSR such as robust feature extraction, feature normalization, robust speaker modeling, model compensation, dereverberation and score normalization are discussed to overcome the speech degradation components i.e., reverberation and ambient noise. Performance results on DSR show that whenever speaker to microphone distant increases, recognition rates decreases and equal error rate (EER) increases. Finally, the paper concludes that applying robust feature and robust speaker model varying lesser with distant, can improve the DSR performance.

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