Blind Signal-to-Noise Ratio Estimation of Speech Based on Vector Quantizer Classifiers and Decision Level Fusion

Board # 29 : A PATTERN RECOGNITION APPROACH TO SIGNAL TO NOISE RATIO ESTIMATION OF SPEECH

“…The first task is to achieve a software implementation of the SNR estimation system described in [1]. The performance of this system will be used as the baseline and students will comprehend how to achieve a modular design.…”

“…The performance of this system will be used as the baseline and students will comprehend how to achieve a modular design. The block diagram of this system [1] is shown in Figure 1. Due to the time-varying dynamics of the speech signal, the students learn how to perform frame by frame processing as illustrated in Figure 2 [8].…”

“…For each utterance, the absolute error which equals |True SNR value -Estimated SNR value| is determined. For each SNR, there are 450 absolute error values which are averaged to from an average absolute error (AAE) [1]. The testing of the system is performed for SNR values from 0 dB to 30 dB in increments of 1 dB.…”

“…Moreover, performing a blind SNR estimate [1] without knowledge of a clean reference signal is more relevant to many practical scenarios especially in the area of voice biometrics [2]. Blind SNR estimation is very useful in biometric speaker identification systems in which a confidence metric is determined along with the speaker identity [3].…”

“…The confidence metric is partially based on the mismatch between the training and testing conditions of the speaker identification system and SNR estimation is very important in evaluating the degree of this mismatch. A pattern recognition approach to estimate the SNR of speech based on vocal tract features and a vector quantizer classifier has been proposed [1]. This system has also been used as a pre-processing step in speaker identification and speaker verification systems [4][5].…”