Speaker recognition Essay

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The real-time implementation of speaker recognition technology involves multiple application-specific tradeoffs such as cost, performance, robustness, enrolment procedures, time for training, adaptability, response time, etc.[2]. Since database sizes for the real-world recognition tasks are ever-increasing, the large population speaker recognition systems pose the challenges like large training time, vast memory requirements and poor response time [3]. Thought the accuracy is always the first consideration, efficient recognition and adaptability are also significant aspects in many real-world speaker recognition systems under large-scale data conditions. This motivates to investigate the new methods in various stages which are involved in a typical speaker recognition system.
The typical speaker recognition process mainly consists of two phases, namely; training (also known as enrolment) and identification [4]. The enrolment stage collects the speaker-specific information of speech signal in a chronological mode to develop speaker models. The cluster of such models in turn establishes the speaker database for the later test phase. During the identification phase, an anonymous speaker model is compared with the existing database and then the results are expedited. In fact, both the phases comprises of the feature extraction which transforms the raw speech signal into a compact but effectual representation that is comparatively more stable and discriminative than the original signal. A typical speaker recognition system comprises of the following phases: feature extraction, dimensionality reduction and classification [3]. In this work, in view of development of the effective large-scale recognition system, state-of-the-art methods have been proposed and conducted experimental investigations in each phase.
Speaker characteristics in the speech signal are differentiated by the dimensions of the vocal tract, characteristics of vocal excitation and learning habits of the speakers [6]. The physiological structure of the speech production system is reflected by the vocal tract characteristics and is relatively more robust and less prone to the mimicry [7]. Therefore, recognition systems mostly use vocal tract information related features like Mel Frequency Cepstral Coeffients (MFCC), Linear Predictive Cepstral Coding (LPCC) and other non-conventional features [8]. These methods have utilized information present in the linear predictive (LP) residual signal. The features taken from the LP-residue are then joined to the MFCC or the LPCC. Two approaches known as temporal and frequential representations are examined. The former consists of an auto-regressive (AR) modeling of the signal followed by a cepstral transformation in a similar way to the LPC–LPCC transformation [9]. However, under noisy environments and large datasets, performance of vocal tract information related features severely degrades [10]. Hence, it is necessary to derive robust...

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