In addition to visual and auditory evaluation, forensic audio experts use automatic speaker verification systems to analyze forensic voice evidence. Gaussian Mixture Model is widely used in such systems. Recently, the usage of i-vector, derived from factor analysis, has gained popularity. In this work, performance comparison of Gaussian Mixture Model and i-vector methods on a speaker verification system is conducted on short recordings of speech. The experimental results revealed that the system that uses i-vector produces a much lower equal error rate
Abstract. Speaker recognition systems frequently use GMM-MAP method for modeling speakers. This meth...
This paper presents a simplified and supervised i-vector modeling framework that is applied in the t...
Physiological and behavioural human characteristics are exploited in biometrics and performance metr...
Abstract — This paper presents a performance evaluation of two classification systems for text indep...
The introduction of Gaussian Mixture Models (GMMs) in the field of speaker verification has led to v...
Due to copyright restrictions, the access to the full text of this article is only available via sub...
In this paper, two models, the I-vector and the Gaussian Mixture Model-Universal Background Model (G...
The development in the interface of smart devices has lead to voice interactive systems. An addition...
This paper presents a generalized i-vector representation frame-work using the mixture of Gaussian (...
Voice recognition has become a more focused and researched field in the last century,and new techniq...
Robust speaker verification on short utterances remains a key consideration when deploying automatic...
International audienceThis paper presents an overview of a state-of-the-art text-independent speaker...
In this paper, the performance of speaker modeling schemes such as vector quantization (VQ) and Gaus...
The introduction of Gaussian mixture models in the field of voice recognition systems has establishe...
Speaker recognition is a biometric operation of accepting a claimed person based on analyzing his sp...
Abstract. Speaker recognition systems frequently use GMM-MAP method for modeling speakers. This meth...
This paper presents a simplified and supervised i-vector modeling framework that is applied in the t...
Physiological and behavioural human characteristics are exploited in biometrics and performance metr...
Abstract — This paper presents a performance evaluation of two classification systems for text indep...
The introduction of Gaussian Mixture Models (GMMs) in the field of speaker verification has led to v...
Due to copyright restrictions, the access to the full text of this article is only available via sub...
In this paper, two models, the I-vector and the Gaussian Mixture Model-Universal Background Model (G...
The development in the interface of smart devices has lead to voice interactive systems. An addition...
This paper presents a generalized i-vector representation frame-work using the mixture of Gaussian (...
Voice recognition has become a more focused and researched field in the last century,and new techniq...
Robust speaker verification on short utterances remains a key consideration when deploying automatic...
International audienceThis paper presents an overview of a state-of-the-art text-independent speaker...
In this paper, the performance of speaker modeling schemes such as vector quantization (VQ) and Gaus...
The introduction of Gaussian mixture models in the field of voice recognition systems has establishe...
Speaker recognition is a biometric operation of accepting a claimed person based on analyzing his sp...
Abstract. Speaker recognition systems frequently use GMM-MAP method for modeling speakers. This meth...
This paper presents a simplified and supervised i-vector modeling framework that is applied in the t...
Physiological and behavioural human characteristics are exploited in biometrics and performance metr...