Review of various stages in speaker recognition system, performance measures and recognition toolkits

Pawar, Rupali; Jalnekar, Rajesh; Chitode, J. S.

doi:10.1007/s10470-017-1069-1

Cited by 27 publications

(11 citation statements)

References 25 publications

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“…Modern and newly published deep learning-based feature extraction approaches, ASR algorithms are extensively explained in this paper. Besides, a few other surveys are introduced in the speaker recognition domain at different times [28]- [47]. As these surveys did not precisely uphold the speaker recognition domain, an extensive study in this domain was necessary.…”

Section: Reference Year Main Purpose Challengesmentioning

confidence: 99%

“…There are also two possible conditions: Y , which means that the expression is acknowledged as the speaker's, and N , meaning that the expression is refused. From these expressions, the conditional probability can be summarized as [28]: P (Y |y) is the probability of correct acceptance, P (Y |n) the probability of false acceptance (FA), P (N |y) the probability of false rejection (FR), P (N |n) the probability of correct rejection. The relationships between these parameters: P (Y |y) + P (N |y) = 1 and P (Y |n) + P (N |n) = 1.…”

Section: B Equal Error Ratementioning

confidence: 99%

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A Survey of Speaker Recognition: Fundamental Theories, Recognition Methods and Opportunities

et al. 2021

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Humans can identify a speaker by listening to their voice, over the telephone, or on any digital devices. Acquiring this congenital human competency, authentication technologies based on voice biometrics, such as automatic speaker recognition (ASR), have been introduced. An ASR recognizes speakers by analyzing speech signals and characteristics extracted from speaker's voices. ASR has recently become an effective research area as an essential aspect of voice biometrics. Specifically, this literature survey gives a concise introduction to ASR and provides an overview of the general architectures dealing with speaker recognition technologies, and upholds the past, present, and future research trends in this area. This paper briefly describes all the main aspects of ASR, such as speaker identification, verification, diarization etc. Further, the performance of current speaker recognition systems are investigated in this survey with the limitations and possible ways of improvement. Finally, a few unsolved challenges of speaker recognition are presented at the closure of this survey.

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Section: Reference Year Main Purpose Challengesmentioning

confidence: 99%

Section: B Equal Error Ratementioning

confidence: 99%

A Survey of Speaker Recognition: Fundamental Theories, Recognition Methods and Opportunities

et al. 2021

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“…The first stochastic process is the Markov chain which is characterized by states and transition probabilities wherein the states were hidden. On the other hand, the second stochastic process produces emissions observable at each moment, depending on a state-dependent probability distribution [4], [13], [18], [20]. The development of Hidden Markov Models is widely used in various studies all over the world.…”

Section: Hidden Markov Modelmentioning

confidence: 99%

“…The intercept and the weights obtained from the neural networks as discussed in Section III was used to create the enhanced model as shown in (12). The specific model for both Apple and Nokia Data was presented in (13) and (14). Using the model in (13) and (14), the results of the prediction of close price for two weeks for both Apple and Nokia data was shown in Table 4.…”

Section: Hidden Markov Modelmentioning

confidence: 99%

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Application of Enhanced Hidden Markov Model in Stock Price Prediction

Acula

Guzman

2020

J. Mod. Sim. Mater.

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The main focus of this research is the enhancement of the Hidden Markov Model by using some features of Neural Networks and the forecasted values of predictors by Seasonal Autoregressive Integrated Moving Average. The enhanced method was used to predict the close price of stocks whose predictors are open price, high price, low price, and volume of Apple and Nokia data. The performance of the method was measured using the Mean Absolute Percentage Error of the predicted price. The result was compared against the actual close price by using the paired T-test. The testing of the hypothesis showed that the Enhanced Hidden Markov Model obtained more than 94% accuracy rate. It also shows that in Apple data, the predicted close price of the Enhanced Hidden Markov Model is significantly better than the predicted close price of Neural Networks. Using Nokia data, the test claims that there is no difference between the performance of Enhanced Hidden Markov Model and Neural Network in prediction.

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A Hybrid Data Fusion Architecture for BINDI: A Wearable Solution to Combat Gender-Based Violence

Rituerto-González

Miranda

Canabal

et al. 2020

Communications in Computer and Information Science

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Review of various stages in speaker recognition system, performance measures and recognition toolkits

Cited by 27 publications

References 25 publications

A Survey of Speaker Recognition: Fundamental Theories, Recognition Methods and Opportunities

A Survey of Speaker Recognition: Fundamental Theories, Recognition Methods and Opportunities

Application of Enhanced Hidden Markov Model in Stock Price Prediction

A Hybrid Data Fusion Architecture for BINDI: A Wearable Solution to Combat Gender-Based Violence

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