• Umaisa Hassan
• Sukhvinder Kaur
• Dr. Rahul Malhotra

Speech is the most usual form of human communication and is an individual’s unique characteristics. Speech processing has emerged as one of the important application area of digital signal processing. The objective of automatic speaker recognition is to extract, characterize and recognize the information about speaker identity. In this, the features of speech signal are first extracted using Discrete wavelets transform (DWT), Linear Predictive Coefficients (LPC), Irregular Discrete Wavelet Packet Transform (DPWT), Pyknogram, Mel Frequency cepstral coefficient (MFCC)). For verifications these features are matched using feature matching algorithms T-Test distance matrix, Kullback–Leibler Distance Metric (KL2), and Bayesian Information Criterion (BIC). Its performance can be evaluated by using Detection Error Trade-off (DET) & Receiver Operating Characteristics (ROC) curve. It can be implemented in biometric, Access Control, transaction authentication, Law Enforcement, speech data management.

Discrete wavelet transform(DWT), Pyknogram, Mel Frequency cepstral coefficient (MFCC), Non-linear energy operator T-Test, Kullback–Leibler Distance Metric (KL2), Bayesian Information Criterion (BIC), Detection Error Trade-off (DET) and Receiver Operating Characteristics (ROC).

"Speaker Recognition Techniques: A Survey", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.47-50, April-2019, Available :http://www.jetir.org/papers/JETIR1904G65.pdf

"Speaker Recognition Techniques: A Survey", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp47-50, April-2019, Available at : http://www.jetir.org/papers/JETIR1904G65.pdf