TRSD: A Time-Varying and Region-Changed Speech Database for Speaker Recognition

Li, Dongdong; Liu, Jinlin; Wang, Zhe; Li, Yanqiong; Chen, Baijun; Cai, Lizhi

doi:10.1007/s00034-022-01964-1

Cited by 5 publications

(1 citation statement)

References 51 publications

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“…As a corollary, a slew of open-source implementations [7,11] are actively being incorporated. TensorFlow [37], PyTorch [38], ASVtorch [39], Alize [40], Kaldi [16] and HTK [41] are a few of the most prominent options. Using these toolkits will benefit DL research and production because they allow for flexible model creation at a big scale and in varied situations.…”

Section: Ubm-gmmmentioning

confidence: 99%

Analysis of Human Voice for Speaker Recognition: Concepts and Advancement

Sumit Srivastava

2024

jes

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Human voice or speech is a contactless, non-invasive biometric trait for human recognition, easy to use with minimal computer complexity and inexpensive to implement. Speaker recognition (SR) has turned out to be a magnificent approach using speech as the central premise since decades. Its broad range of usages, like forensic speech verification to identify culprits by law enforcement authorities and access control to mobile banking, mobile shopping, etc., has made it a lucrative area of research. Also, the ease of use and dependability of SR will significantly assist people with disabilities in securely accessing and reaping the benefits of digital-era services. Additionally, the emergence of numerous deep learning methods for feature extraction and classification, has helped SR to achieve tremendous progress. This paper presents a comprehensive study on the progression of SR for decades till the present, including integration with Blockchain and challenges. It covers most of the factors that influence SR performance such as fundamentals and structure of SR, different speech pre-processing techniques, various speech features, feature extraction techniques, traditional and neural network-based classification techniques and deep learning-based SR toolkits. As a consequence, in this digital Blockchain era, it will help to design robust and reliable recognition-based services for mankind.

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Section: Ubm-gmmmentioning

confidence: 99%

Analysis of Human Voice for Speaker Recognition: Concepts and Advancement

Sumit Srivastava

2024

jes

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Design of intelligent behavior analysis software based on speaker identity classification algorithm in microgrid mode

Guo

2024

Adv Control Appl

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Digital technology still has a low level of intelligence in the microgrid mode of teaching behavior analysis, resulting in the traditional manual observation and recording stage still being used for speaker identity classification, and the efficiency of teaching behavior analysis is also low. In response to the above issues, the research is based on the teacher‐student analysis method and proposes a dual clustering algorithm based on the general background model Gaussian mixture model for speaker identity classification, thereby realizing the development and design of intelligent behavior analysis software. The research results indicate that the average recall rate of behavior transition points in the classroom teaching discourse corpus of the intelligent behavior analysis software is 89.03%, which is better than traditional analysis methods. Therefore, the intelligent behavior analysis software constructed by the dual clustering algorithm has high effectiveness and practicality. The research proposes a method model and implements intelligent visualization for classroom teaching behavior analysis, improving the efficiency of analyzing current microgrid teaching behavior.

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