Effectiveness of linear prediction characteristics of the speech wave for automatic speaker identification and verification

Abstract

Several different parametric representations of speech derived from the linear prediction model are examined for their effectiveness for automatic recognition of speakers from their voices. Twelve predictor coefficients were determined approximately once every 50 msec from speech sampled at 10 kHz. The predictor coefficients and other speech parameters derived from them, such as the impulse response function, the autocorrelation function, the area function, and the cepstrum function were used as input to an automatic speaker-recognition system. The speech data consisted of 60 utterances, consisting of six repetitions of the same sentence spoken by 10 speakers. The identification decision was based on the distance of the test sample vector from the reference vector for different speakers in the population; the speaker corresponding to the reference vector with the smallest distance was judged to be the unknown speaker. In verification, the speaker was verified if the distance between the test sample vector and the reference vector for the claimed speaker was less than a fixed threshold. Among all the parameters investigated, the cepstrum was found to be the most effective, providing an identification accuracy of 70% for speech 50 msec in duration, which increased to more than 98% for a duration of 0.5 sec. Using the same speech data, the verification accuracy was found to be approximately 83% for a duration of 50 msec, increasing to 98% for a duration of 1 sec. In a separate study to determine the feasibility of text-independent speaker identification, an identification accuracy of 93% was achieved for speech 2 sec in duration even though the texts of the test and reference samples were different.

On the asymptotic statistical behavior of empirical cepstral coefficients

“…1 500 500 i = l has been computed and the empirical covariance matrix p over the 500 examples has been calculated similarly to (2). Table I1 is the inverse Fourier transform of the product of l / S L ( w ) and Al?(T)e-Jrw.…”

“…. '-, a,} is the state set, g: S x 63 --t S is the state transition function, and h: S -+ Q. is the output function; explicitly, (s,, b,), a, = h(sJ (2) where s,, b,, and a, denote the state, input, and output processes.…”

“…The general problem of synthesizing an assigned rational spectral density by means of an SM fed by a suitable input formed by independent and identically distributed (i.i.d.) symbols was dealt with by Mullis and Roberts [2], who proved that such a synthesis is possible for any rational spectral density. Unfortunately, the proof is not constructive and does not convey any suggestion about the choice of the SM and of the input probabilities.…”

“…types of feature vectors, the cepstrum provides the best performance in speech recognition [6] and speaker verification [2] applications.…”

Robust Speaker Identification System Based on Wavelet Transform and Gaussian Mixture Model

“…Linear predictive coding (LPC) is used because of its simplicity and effectiveness in speaker/speech recognition [1,2]. Another widely used feature parameters, mel frequency cepstral coefficients (MFCC), are used [3] because they are calculated by using a filter-bank approach in which the set of filters has equal bandwidth with respect to the mel-scale frequencies.…”