The SI task is further categorized as text-dependent SI and text-independent SI tasks. In SI, the task is to classify an unlabeled voice token as belonging to one of a set of n reference speakers (i.e., one-to-many matching task), whereas SV refers to the task of deciding whether an unlabeled voice token belongs to a specific reference speaker with two possible outcomes that the token is either accepted or rejected.
#Wavesurfer 1.8.5 microsoft verification
The SR task is basically categorized into two specific tasks: (i) speaker identification (SI) and (ii) speaker verification (SV). SR is a generic term that refers to any task that discriminates between people based on their voice characteristics. The key advantage of using biometrics is that it is more reliable than conventional artifacts, perhaps even unique moreover, biometric attributes cannot be lost or forgotten and thus need not be remembered.
The art of identifying people based on their voice characteristics is of paramount importance owing to the growing need in information processing, telecommunications, and more particularly true for security applications such as physical access control, computer data access control, forensic, military, etc. The key motivation behind the study of speaker recognition (SR) is to ensure more reliable personal identification based on the speaker’s voice. Recognition of a speaker by using the intrinsic characteristics of his/her voice is an example of a biometric task. The experimental results showed that the proposed SI task with the VAD algorithm using ZFFPR and EMD at its front end performs better than the SI task with short-term energy-based VAD when used at its front end, and is somewhat encouraging. In both cases, widely accepted Mel frequency cepstral coefficients are computed by employing frame processing (20-ms frame size and 10-ms frame shift) from the extracted voiced speech regions using the respective VAD techniques from the realistic speech utterances, and are used as a feature vector for speaker modeling using popular Gaussian mixture models. The performance of this proposed SI task is compared against the traditional energy-based VAD in terms of percentage identification rate. In this article, the efficacy of an EMD-based VAD algorithm is studied at the front end of a text-independent language-independent SI task for the speaker’s data collected in three languages at five different places, such as home, street, laboratory, college campus, and restaurant, under realistic conditions using EDIROL-R09 HR, a 24-bit wav/MP3 recorder. Recently, we have developed a VAD algorithm using a zero-frequency filter-assisted peaking resonator (ZFFPR) and EMD. EMD-based VAD has become an important adaptive subsystem of the SR system that mostly mitigates the effect of mismatch between the training and the testing phase. Recently, speech data analysis and speech data processing for speech recognition and SR tasks using EMD have been increasing. EMD is an a posteriori, adaptive, data analysis tool used in time domain that is widely accepted by the research community. Huang’s empirical mode decomposition (EMD) combined with Hilbert transform, commonly referred to as Hilbert–Huang transform (HHT), has become an emerging trend. Recently, speech data analysis and processing using Norden E. The performance of most VADs deteriorates at the front end of the SR task or system under degraded conditions or in realistic conditions where noise plays a major role. Any SR system uses a voice activity detector (VAD) as the front-end subsystem of the whole system. In such cases, the performance of speaker identification (SI) or speaker verification (SV) degrades considerably under realistic conditions. Also, in real conditions, the nature of the noise present in speech data is not known a priori. Speech signal is nonlinear and nonstationary, and therefore, difficult to analyze under realistic conditions. Speaker recognition (SR) under mismatched conditions is a challenging task.
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