Speech Magnitude-Spectrum Information-Entropy (MSIE) for Automatic

Ave. Eugenio Garza Sada 2501 Sur, Col. Tecnolo´gico, Monterrey, NL, Me´xico, CP 64849 {jnolazco, aceves, paola.garcia}@itesm.mx
Abstract
The Magnitude-Spectrum Information-Entropy (MSIE) of the speech signal is presented as an alternative representation of the speech that can be used to mitigate the mismatch between training and testing conditions. The speech-magnitude spectrum is considered as a random variable from which entropy coefficients can be calculated for each frame. By concatenating these entropic coefficients to its corresponding MFCC vector, then calculating the dynamic coefficients, Δ and ΔΔ, the results show an improvement compared to a baseline. The MSIE effectiveness was tested under the Aurora 2 database audio files. When trained in clean speech, the experimental results obtained by the MSIE concatenated to the MFCC outperform the results obtained with the MFCC baseline system for selected types of noises at different SNRs. For this selected group of noises the overall improvement performance in the range 0 dB to 20 dB for the Aurora 2 database is of 15.06%.
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DOI 10.1109/ICPR.2010.1061
process, a PMF is calculated, but instead of the enery spectrum they use the entropy of the complex spectrum. This complex spectrum is treated as a two-dimensional signal, being the real part of the spectrum one dimension, and the imaginary part of the spectrum the other dimension. Therefore entropy is computed as
p(ω) =
Se(ω)
ω=N ω=1
Se(ω)
,
(2)
where Se(ω) represents the energy of the ω-th frequency component of the spectrum, p(ω) is the PMF of the energy spectrum, and N is the total number of frequency components. This equation is later used for
(5)
where n is the n-esim signal frame.
s(t)
FFT ( ) S()

ln(2e)
+
1 2
ln

x2
2 y


2 xy
H(i)



FC (n)
Figure 2. Camarena entropy-based coding system.
computing full-band or multiband entropy. The com-
plete process is shown in Figure 1.
( ) ( ) ( ) s t
FFT S()

Se () Se () p()
H ( ) N
=1Se
2010 International Conference on Pattern Recognition
Speech Magnitude-Spectrum Information-Entropy (MSIE) for Automatic Speech Recognition in Noisy Environments
Juan A. Nolazo-Flores1, Roberto A. Aceves L.1, L. Paola Garc´ıa-Perera1 ∗ 1Computer Science Department.
Instituto Tecnolo´gico y de Estudios Superiores de Monterrey, Campus Monterrey,
ω=N
H(S∗(ω)) = − p(S∗(ω)) ln (p(S∗(ω))), (3)
ω=1
where H(S∗(ω)) is the complex spectrum entropy. Moreover, they assume that the spectrum (or part of the spectrum) follows a Gaussian distribution, so the mean and the variance are used to calculate the entropy as follows,
H (S ∗ (ω(ln(σx2
σy2
−
σx2y )),
(4)
where σx2 and σy2 are the variances of the real and imaginary part respectively, and σx2y is the covariance between the real and imaginary part of the spectrum. Fi-
The paper is organized as follows. Section 2 defines MSIE. Section 3 describes the setup of the experiments to test the efficency of MSIE. Section 4 shows the results of those experiments. Finally, Section 5 opens the future discussion and gives conclusions of the study.
p
Hi
Figure 1. Misra entropy calculation.
Camarena [1] calculates an entroy-based acoustic binary code to represent music (Figure 2). As part of their
1051-4651/10 $26.00 © 2010 IEEE
nally, to compute the acoustic binary code, they em-
ployed the sign function of the entropy slope, obtaining FC (n), defined as
FC (n) =
1 0
if [h(n) − h(n − 1)] > 0 , Otherwise
2 Methodology
2.1 Magnitude Spectrum Entropy
Information-
1. Introduction
In pattern recognition, classification needs relevant feature vectors. Feature vectors are numerical representations of the objects’ properties. For Automatic Speech Recognition (ASR) task, the Mel Frequency Cepstral Coefficients (MFCCs) have shown to be very robust feature vectors and are by far the dominant parameters. Other parametrizations and enhancement algorithms are PLP (Perceptual Linear Predictive) [3] and RASTA (RelAtive SpecTrAl) [4]. The purpose of this
The entropy is defined as
ω=N
H(ω) = − p(ω) log2(p(ω)).
(1)
ω=1
Therefore, a key element to evaluate the entropy lies on obtaining the probability mass function (PMF), p(ω). This PMF can be derived in different ways, in [5], e.g., the energy spectrum is directly scaled such that it represents a probability mass function (PMF) as follows