PROCEEDINGS of the23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

Performance evaluation of autocorrelation technique for automatic
speaker identification in various environments
Noha KORANY1
1

Alexandria University, Egypt

ABSTRACTThis paper aims to improve the performance of automatic speaker identification in various environments.
Feature extraction is the first stage for the identification process. It is a challenge to extract those features that
express the speakers’ individualities. The speaker variation is highly related to the spectrum of its speech
signals. Hence, the autocorrelation technique and the discrete cosine transform are applied for the feature
extraction of speech signals. Then, those features are employed by a classifier engine to determine thespeaker identity. In this paper, Gaussian mixture model is used as the recognition engine. The performance ofthe speaker identification system is investigated for various settings of the applied technique. Moreover, theeffect of additive and convolutional noise on the performance of the identification process is investigated.
Keywords: Identification, Speech, Autocorrelation

1. INTRODUCTION
The speaker identification process aimed to identify the speaker based on his speech signals.
Automatic speaker identification consists of two stages: feature extraction and feature classification
by means of a recognition engine. Many features have been extracted and successfully employed by
the classifier for the recognition purpose. Mel-frequency cepstral coefficients (MFCC), perceptual
linear predictive cepstral coefficients (PLPCC) have proved good results for identifying speakers inclean environments (1). Another techniques are suggested to improve the identification process innoisy environments (2, 3).
The speech spectrum reflects the speaker characteristics as well as it carries the message
information. These characteristics are related to the physical system of speech production forindividuals. Hence, the feature extracted from the spectral characteristics of the speech signals are themost useful ones for automatic determination of the speaker identity. The paper applies theautocorrelation technique and the discrete cosine transform (DCT) for the feature extraction stage.
This autocorrelation-based DCT feature has been specified for ships’ classification by means of thenoise produced by their platform (4).
The paper aims to determine the spectral components that fit the best for the identification process.
The performance of the identification process is investigated in reverberant environments and in thepresence of additive white Gaussian noise. The paper is organized as follows. The next section
discusses the speech production mechanism and its relation to speaker individual ity. Section 3
describes the technique used for feature extraction. Section 4 presents the database used, whereas
section 5 describes the experiments and discuss the results. Finally, section 6 presents the paper
summary and the final conclusions.

2. SPEECH PRODUCTION AND SPEAKER INDIVIDUALITY
Speech production takes place in two separate processes. The excitation process which consists ofvibration of vocal folds for voiced sounds or noise turbulence for unvoiced sounds. In the second
process, the sound is filtered through vocal and nasal tracts and it is radiated from the lips and nostrils.
Speaker variation is related to difference in vocal tract length, and vocal folds va riation in both size
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Noha.korany@alexu.edu.eg

and mass. These variations between speakers affect the pitch of voiced sounds and their resonance(formant) frequencies (5).
The voiced pitch corresponds to the fundamental frequency (F0). Large variation in F0 between
speakers enables normal listeners to identify them. Filter bank configurations a re chosen for spectral
coding of speech signals. The low frequency region (100 – 500 Hz) provides better spectral coding ofF0, whereas the middle frequency region (500 – 1500 Hz) represents the vowel formants. The highfrequency region (1500 – 5000 Hz) represents the temporal speech aspects (6).

3. FEATURE EXTRACTION TECHNIQUE
The speech spectrum carries the message information and the individual characteristics of thespeaker. Figure 1 shows the spectrum of a speech segment for two different speaker s and same output
sound. It is obvious that the variation in speech spectrum is related to speaker discrimination.

Figure 1 – Spectrum of speech segment for two different speakers.
The power spectral density describes the distribution of the power of the speech signal withfrequency. For a speech segment x(t), the power spectral density ܵ௫ ሺ݂ሻ is defined in the following
equation, where ܴ௫ ሺ߬ሻ is the autocorrelation function for the speech segment, and ࣠ denotes Fouriertransform.
(1)ܵ௫ ሺ݂ሻ ൌ ࣠൫ܴ௫ ሺ߬ሻ൯It is aimed to extract features that represent the strength of the variations with frequency, and hence
characterize the speakers. This is done by applying the autocorrelation function and the discrete cosine
transform (DCT) to the speech signals (4). Figure 2 shows the feature extraction method. First, theautocorrelation function of the speech signal is computed, and then windowed using hamming windowof 30 ms long. Finally, DCT is applied to the windowed signal, and the autocorrelation -based DCTfeature vector is obtained for each segment.

x(n)Autocorrelationܴ௫ ሺ݇ሻ

Windowing

DCT

Autocorrelation-based
DCT feature vectors

Figure 2 – Block diagram of the feature extraction technique.

4. DATABASE
The database consists of sound produced by twelve speakers, eleven males and one female. Eachspeech signal is sampled at 8000 Hz; 16-bit quantization level is used. Clean signals are used for thetraining phase, whereas reverberant signals and reverberant signals plus additive white Gaussian noiseare employed for the test phase. Room reverberation is simulated using comb filters (7). Four room
impulse responses are simulated for reverberation time that varies from 1 to 3 s. Voxforge database isused. A set of twelve spoken sentences is directly used within the training phase. Another set of twelve
spoken sentences is chosen, and convoluted with each of the simulated room impulse response to
obtain four sets of reverberant speech signals. Finally, additive white Gaussian noi se is added to eachspeech signal of a certain reverberant set, and hence a fifth set of noisy speech signals is obtained.
Those five sets of noisy speech signals are used for the test phase.

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5. EXPERIMENTS AND RESULTS
The autocorrelation-based DCT feature vectors for clean and noisy speech signals are extracted,
then they are employed by the Gaussian mixture model for the identification problem (8, 9). Two
Gaussian components are used (10). Clean signals are used for the training phase, whereas the noisy
ones are employed for the testing phase. The five sets of various type of noisy signals are used, and theidentification rate is calculated using each set of these noisy signals. Three experiments are conducted
to investigate the performance of the identification process in noisy environments. The experiments
aim to specify the relevant frequency band that fits the best for automatic speaker identification invarious noisy environments. The following equation relates the coefficient number, k, to its frequency,
f. fs is the sampling frequency, and N is the number of samples per frame.
k 2 N 1 f fs
(2)
The first experiment aims to determine the effect of the number of the autocorrelation-based DCT
coefficients on the identification rate. The feature vectors are extracted from reverberant speech
signals, and they are employed by the classifier within the test phase. The number of coefficientsvaries from 12 to 256 according to the number of octave bands used. The DC coefficient is discarded.
Table 1 shows the cutoff frequencies, the corresponding number of coefficients used, and the resulted
identification rate for the various values of reverberation time .
Table 1 – The identification rate versus number of coefficients for four set of reverberant testing signals
Identification rate, %, for reverberation time of

Cutofffrequency,
Hz
177

Number ofcoefficients
used
11

1s
83.33

1.5 s
66.67

2s
83.33

3s
66.67

354

23

83.33

75.00

91.67

75.00

707

45

91.67

75.00

83.33

83.33

1414

90

75.00

75.00

83.33

66.67

2828

180

66.67

75.00

83.33

66.67

4000

255

66.67

66.67

66.67

66.67

Table 1 concludes that the coefficients that correspond to the low frequency region (88 – 707 Hz)
yields to the highest identification rate for various set of reverberant testing speech signals. For
reverberation time of 1 s, maximum identification rate of 91.67% is reached when 45
autocorrelation-based DCT coefficients are used. Those 45 coefficients correspond to a cutoff
frequency of 707 Hz. Similar conclusion is showed for reverberation time of 3s, and a maximum
identification rate of 83.33% is obtained. In the case of 2s reverberation time, maximum identif ication
rate of 91.67% is reached for 23 coefficients that correspond to a cutoff frequency of 354 Hz.
The second experiment is conducted to determine the number of coefficient s within the low
frequency region (100 – 500 Hz), which improve the identification rate in reverberant environments.
Table 2 shows the cutoff frequencies for filter-bank within the low frequency region (6), thecorresponding number of coefficients used, and the resulted identification rate for the various valuesof reverberation time. It concludes that maximum identification rate is reached for 19
autocorrelation-based DCT coefficients that correspond to a cutoff frequency of 300 Hz. Maximum
identification rate of 91.67% and 100% is reached for reverberant testing signals at rever beration timeof 1s and 2s respectively.
The third experiment is conducted to evaluate the effect of the presence of both additive andreverberant noise on the identification rate. In this experiment, additive white Gaussian noise is added
to the reverberant speech signals and the fifth set of noisy signals is obtained. Then, 20 coefficients areextracted from those noisy signals and are employed by the classifier within the test phase. Figure 3
shows the identification rate versus various values of signal-to-noise ratio (SNR). It is found that highidentification rate is obtained for SNR that equals to or greater than 5 dB. At 0 dB SNR, theidentification rate is decreased to 58.33%.

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Table 2 – The identification rate within the low frequency region for four set of reverberant testing signals
Identification rate, %, for reverberation time ofCutoff
frequency,
Hz
100

Number ofcoefficients
used
61s
58.33

1.5 s
58.33

2s
75.00

3s
66.67

167

1183.33

66.67

83.33

75.00

233

15

83.33

75.00

83.33

75.00

300

1991.67

75.00

100

75.00

367

2375.0075.00

91.67

75.00

433

28

83.33

75.00

91.67

75.00

Figure 3 – Identification rate versus SNR, 20 coefficients extracted, reverberation time = 2s.

6. CONCLUSIONSThe paper discusses the problem of automatic speaker identification in various noisy environments.
The autocorrelation method and the discrete cosine transform are applied to extract the speech featurevectors and those vectors are employed by Gaussian mixture model to determine the speaker identity.
Clean speech signals are used for the training phase, whereas reverberant speech signals are used
for the test phase. The reverberation time is varied to construct various sets of testing signals, and theidentification rate is calculated for each testing set. The paper specifies extracting theautocorrelation-based DCT coefficients that correspond to the low frequency band of cutoff frequencyof 300 Hz as it yields to the highest identification rate.
Moreover, the effect of both reverberation and additive noise is investigated for various SNR. It isfound that employing the autocorrelation-based DCT coefficients that correspond to the specified low
frequency band improves the performance of the identification process for SNR equals or greater than
5 dB.

REFERENCES
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4. Korany N, Classification of ships using autocorrelation technique for feature extraction of theunderwater acoustic noise. Proc Inter-noise 2016; Hamburg, Germany 2016. p. 7097-7102.

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5. Mueller Ch., Speaker Classification I - Fundamentals, Features, and Methods. Berlin Heidelberg,
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