Score Normalisation in Voice Biometrics (CASE STUDY) Essay

Score Normalisation in Voice Biometrics (CASE STUDY) - canvass ExampleStandardisations of nock distributions include Z-norm, and T-norm. Score normalisation helps achieve separation between score distributions of known and unknown speakers. A reduction in tally error rate is achieved by the use of score normalisation methods.Speaker recognition is required in applications, such as operating in environments that ar uncontrolled or while transmitting speech over communication channels. Speaker verification hires assessment of similarity scores between registered or unregistered users and reference models. The expectation is that verification scores should be high for true speakers and low for impostors. However, true speaker verification scores could be adversely alter by background noise, speech variations of the speaker, variations caused by the recording apparatus, and/or effects caused by the communication channel. Score distribution plots enable observation of true speaker scores and impostor scores relative to each other.Test utterances from true speakers and impostors obtained experimentally can be used to generate score distribution plots (see fig. 1). Since, there is an overlap between true and impostor score distributions, an acceptance threshold is chosen. The accuracy of verification process is directly proportional to the distance between the score distributions. Overlapping of score distributions could result in errors, such as pretended acceptances and false rejections. False acceptances involve accepting impostors as true speakers. False rejections involve rejecting true speakers. Adjusting the threshold could result in reduction of one type of error while increasing the other. This could be overcome by setting the threshold, so that the two error types are equal. This technique is known as the equal error rate (see fig. 2), where false acceptance rate is set equal to false rejection rate.Variations in speech characteristics are caused