A New Method of Voiced/Unvoiced Classification Based on Clustering

doi:10.4236/jsip.2011.24048

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Journal of Signal and Information Processing, 2011, 2, 336-347

doi:10.4236/jsip.2011.24048 Published Online November 2011 (http://www.SciRP.org/journal/jsip)

A New Method of Voiced/Unvoiced Classification

Based on Clustering

Mojtaba Radmard, Mahdi Hadavi, Mohammad Mahdi Nayebi

Department of Electr ical Engineering, Sharif Univer sity of Techno logy, Tehran , Iran.

Email: {radmard, mahd i_hadavi}@ee.sharif.ir, nay[email protected]

Received June 2nd, 2011; revised October 14th, 2011 ; accep ted October 23rd, 2011.

ABSTRACT

In this paper, a new method for making v/uv decision is developed which uses a multi-feature v/uv classification algo-

rithm based on the analysis of cepstral peak, zero crossing rate, and autocorrelation function (ACF) peak of short-time

segments of the speech signal by using some clustering methods. This v/uv classifier achieved excellent results for iden-

tification of voiced and unvoiced segments of speech.

Keywords: Speech, Voiced, Unvoiced, Clustering, Cepstrum, Autocorrelation, Zero Crossing

1. Introduction

The voiced/unvoieced decision is critical in many speech

analysis/synthesis systems because it is essential to know

whether the speech production system involves vibration

of the vocal cords [1-4]. This decision is required for many

applications, including modeling for analysis/synthesis,

detection of model changes for segmentation p ur p oses a nd

signal characteriza tion for ind exing and recognition appli-

cations [1]. The periodicity of this vibration makes the

voiced segments periodic and so distinguishable from the

noisy-like unvoiced segments [5]. Since the speech signals

are quasi-periodic [6-9], making the decision gets hard.

Other difficulties are represented in [3,10].

Common methods extract a feature from speech seg-

ments and make the v/uv decision according to whether

the value of the feature is above or b elow a pre-determined

threshold. The feature can be the cepstral peak [6,11],

some mel-frequency cepstral coefficients [12,13], energy

of the segments [3,14], zero-crossing rate [3,14], the a uto-

correlation function peak [15,16], or harmonic to noise

ratio in the sinusoidal model of speech signal [17]. Since

each feature has its own disadvanta ges, ne w metho ds tend

to use a c ombin ation of features for v/uv decision [2,3] and

since the value of these feat ures are di fferent for variety o f

speeches, adaptive thresholding have been used in most of

the methods.

Different methods have been used in the field of multi-

feature voicing decision. Atal and Rabiner [3] clustered

the segments into two major groups based on a weighted

Euclidian distance in the feature vector space while the

weight was estimated according to some statistical prop-

erties and the features were considered gaussian distrib-

uted in each cluster. Siegel [18] used a non-statistical

nonparametric classifier to make v/uv decision. In this

method no assumptions are made about the distribution

of the features, and training focuses on patterns near the

boundry between two regions in the feature space, rather

than using statistics to describe each class. Siegel and

Bessey [19] tried to develop the mentioned methods by

using linear discrimination in the feature vector space.

The use of two or more feature s in the voici ng decision

tended to the methods which do not consider the features

as a vector and use the best feature for each frame like the

work in [20] . The work presente d here is in this categor y.

In this work, which is preliminaril y presented in [21], we

use implicitly two thresholds for each feature and make

the decision for each frame based on only one of the fea-

tures that performs better than other features. This paper

is organized as follows. The description of the suggested

algorithm is given in Section 2. Some discussions about

the new method are presented in Section 3. In Section 4,

the results of the algorithm are represented. Section 5

discusses the disadvantages of former methods. Finally,

the conclusion is given in Section 6.

2. The Proposed Algorithm

In this section we will describe our method for V/UV de-

cision. Here we use three features which are the cepstral

peak, autocorrelatio n function (ACF) peak a nd zero cross-

ing rate. The speech signal, sampled at 8 kHz, is analyzed

at 10 ms intervals using a 40 ms Hamming window. Then

A New Method of Voiced/Unvoiced Classification Based on Clustering

337

the following features are extracted and analyzed.

1) Cepstral peaks: The cepstrum, defined as the real

part of the inverse Fourier transform of the log-power

spectrum, has a strong peak corresponding to the pitch

period of the voiced speech segment being analyzed [22].

Here we use a primary normalization to have a fair deci-

sion fo r all of the frames (i ncludin g high ene rgy and lo w

energy frames). A 512-point fast Fourier transform (FFT)

is used and the peak picking scheme is to determine the

cepstral peak in the interval [2.5 - 15 ms], corresponding

to pitch frequencies between 60 - 400 Hz. Since the cep-

stral peaks decrease in amplitude with increasing que-

frency, a linear cepstral weight is applied over the 2.5 to

15 ms. The linear cepstral weighting with range of one to

five was fo und e mpiric ally b y usin g per iodic pulse train s

with varying periods as the input to the program.

2) Zero crossing rate: the method in this part is the

well known met ho d using the formula (1).

( )()

ii i

ZCRsgn xnsgn xn

−

−−

 

 

∑

(1)

It is kno wn than tha t the ZCR of an unvoice d segme nt

is much mor e tha n that of a voiced segment.

3) Auto-correlation function peaks: As we know, the

speech signal is periodic for voiced segments. So, we

make the V/UV decision based on finding a high peak in

this function. But since in this algorithm, this function

should behave fairly for different segments, it should be

normalized, like the cepstral peaks. But, since the speech

signal is originally quasi-periodic at voiced segments and

the noise of the environment is added, the voiced seg-

ments are not precisely periodic. This non-periodicity

emerges in the segments with low energy (because SNR

falls down) or in high frequencies (since noise is masked

in low frequencies but can be destructive in high frequent-

cies). To eliminate the first effect, we use the method of

Center clipping [10], with the clipper's amplitude of 1/3

of the maximum of the absolute of the signal's amplitude.

To eliminate the second one, we use a band-pass filter

with the cut-off frequencies of 20 and 900 Hz.

After determining how each feature is extracted, we go

back to the algorithm. Here the V/UV decision is made in

this way: each of the information groups (that obtained

from features: Cepstral peaks, ACF peaks and zero cro-

ssing rate) is clustered into three clusters by K-Means

algorithm [23]. So after clustering, we have three clusters

for each feature: the first cluster contains the frames in

which the related feature has low values, the second one

contains the frames in which the related feature has av-

erage values and the third one contains the frames in

which the related feature has high values. For example,

the frames in the first cluster of zero crossing have low

ZCR and so, are very likely to be voiced, despite the

third cluster that are very likely to be unvoiced and about

the second cluster we cannot conclude yet. But when we

consider the three features simultaneously, we can decide

about almost all of the frames. It is practically observed

that very little fra mes are found to be in the second clus-

ter for all three features (about 4% of all the frames). We

make the V/UV decision for these frames by clustering

them into two clusters, based on autocorrelation funct ion

(since it works better than the other features as we will

see). Now the only thing remained to do is to decide

what to do about the frames that are voiced in a feature

and unvoiced in another feature. Here, priority gets im-

portant. It means we decide the V/UV of a frame after

giving each cluster a priority. The six rules that we

choose to determine the priorities are as below:

If a frame belongs to the first cluster of zero-crossing

rate, it is voiced .

If a frame belongs to the third cluster of zero-crossing

rate, it is unvoiced.

If a frame belongs to the first cluster of cepstral peaks,

it is unvoiced .

If a frame belongs to the third cluster of cepstral peaks,

it is voiced.

If a frame belongs to the first cluster of ACF peaks, it

is voiced.

If a frame belongs to the third c luster of ACF peaks, it

is unvoiced.

How these rules are given priorities, is described be-

low:

First, for each rule we calculated the error probability,

and the one with the least error probability was chosen as

the first priority. Then, to choose the second priority, we

calculated t he conditional err or prob ability for the rest o f

the rules, on the condition that the first priority is defined

and classifies some frames as voiced or unvoiced (based

on which rule is considered as the first priority). For

these tests, we used some TIMIT files. We continued in

this way until all p riorities are d efined. The results are as

below:

The 1st p r iority: the third clust e r of ACF peaks.

The 2nd priority: the third cluster of cepstral peaks.

The 3rd priority: the third cluster of zero-crossing rate.

The 4th pr iority: the first clust e r of zero-crossing rate.

The 5th pr iority: the first clust e r of ACF peaks.

The 6th pr iority: the first clust e r of cepstral pe a ks.

Also you will see the complete results with the error

prob abilities in t he simulation and e valuation sectio n.

To improve the performance of the clustering algo-

rithm, we used a limiter for each feature. The reason is

that some frames have large values (e.g. ACF peaks) and

this causes the cluster ing algorithm to consider them as a

separate cluster in the third cluster. The upper bound for

each feature is chosen proportional to the mean of that

A New Method of Voiced/Unvoiced Classification Based on Clustering

338

feature in all frames. Also, in order to choose a better

upper bound, we eliminated the silence of the start and

end of the speech. At last, a median filter of order 5 is

found empirically to work well for the resulting V/UV

estimates. The block diagram of the proposed algorithm

is depicted in Fig ur e 1.

3. Discussion

The reason of using three features with three clusters is

that each feature will perform well and accurately, just

when the value of that feature is very high or very low.

So the use of three clusters will help us in this matter.

Also to complete the decision, it is necessary to use more

than one feature. In this case each feature will correct

some of the other features’ mistakes, because each fea-

ture's base is different from the other one.

Also three clusters can be considered as using two

threshold s (similar to double threshol ding in the detection

topics [24]). So it is obvious that using more than one

feature with e ach having two thr esholds will pe rform bet-

ter than using some features with one threshold and that

will work better than using one feature with one thresho ld,

which is usually used to make a V/U V decision.

The deficiencies of different methods and different

features in V/UV decision are discussed below and we

will show the deficiencies and faults of each feature with

some practical samples.

In the cepstral domain, the considerations and tests'

results show that the cepstral peak does not perform well

when the signal has limited bandwidth. Inspite of that,

whe n the signal has high fr equenc y coeffici ents, the cep-

stral peak is a good indicator of being voiced/ unvoiced.

The empirical results that show this can be seen in Fig-

ures 2 and 3.

Figur e 1. The block diagram of the proposed algorithm.

A New Method of Voiced/Unvoiced Classification Based on Clustering

339

number of samples

(a)

number of samples

(b)

number of samples

(c)

Figur e 2. Testing the cepstral feature. (a) The signal with limited bandwidth; (b) The spectral domain; (c) The cep s t ral domain.

A New Method of Voiced/Unvoiced Classification Based on Clustering

340

number of samples

(a)

number of samples

(b)

number of samples

(c)

Figure 3. Testing the cepstral feature. (a) The signal with high frequency coefficients; (b) The spectral domain; (c) The cep-

stral domain.

Also about the ACF, the effects of the vocal source

and vocal tract are convolved with each other in the au-

tocorrelation functions and this results in broad peaks

and in some cases multiple peaks in the autocorrelation

function [22]. Furthermore, the considerations and tests’

results show that the ACF peak does not perform well

whe n the fu nd a me nta l fre q uen c y and the first for mant are

near each other. This fact can be easily seen in Figure 4.

A New Method of Voiced/Unvoiced Classification Based on Clustering

341

number of samples

(a)

number of samples

(b)

number of samples

(c)

Figure 4. Testing the ACF feature. (a) A speec h signal wi th the funda mental f requenc y near the first for mant; (b) The s pec-

tral d omain; (c) The ACF.

A New Method of Voiced/Unvoiced Classification Based on Clustering

342

Besides, there are segments in speech that are not pe-

riodic but are similar to noise. Inspite of that, their ZCR

is low. As an example, the silence segments, between

speech, have very low energy but if classified based on

their ZCR, they are mistakenly marked voiced. Compar-

ing Figures 5(a) and (b) shows thi s fa ct.

In clustering we cluster the values of each feature in

all frames into 3 clusters: low, average and high values.

In fact, when speech is uttered by a specific person, each

feature’s values in the voiced segments are very similar

to each other. This is true about the unvoiced segments

too and this is the base of the clustering method, which

performs very we ll through the tests.

4. Simulations’ Results

In this section we show the simulations’ results and dis-

cuss about the quality of the proposed algorithm. 821

frames of speech, that were taken from TIMIT, were

tested. To calculate the error probability of each of the

rules (the six rules described above with considering the

priorities we defined), we counted the number of frames

that were classified as voiced or unvoiced in each rule

(each priority) based on the priorities we determined.

Then we counted the number of frames, which were

wrongly classified. The frames were labeled visually by

looking at their time domain shape and their frequency

domain spectrum. The results are depicted in Table 1.

Totally the error for voiced segments was 4.8% and the

error for unvoiced segments was 1.1%.

In the above table etc means the number of frames that

are clustered to the second cluster for all three features

and are classified as voiced or unvoiced by the clustering

number of samples

(a)

number of samples

(b)

Figure 5. Testing the ZCR feature. (a) A scilence segment with low energy and low ZCR; (b) An unvoiced segment with high ZCR.

A New Method of Voiced/Unvoiced Classification Based on Clustering

343

Table 1. Simulations ’ result s.

auto-3

(1st priority) ceps-3

(2nd priority) zero-3

(3rd priority) zero-1

(4th pr io rity) auto-1

(5th pr io rity) ceps-1

(6th pr io rity) etc

The number of

frames identified

V or UV 248 46 158 124 142 69 34

The number of

frames wrongly

identified

0 0 1 2 7 10 3

method described in section two (clustering to two clus-

ters based on autocorrelation function). To better under-

stand the above table, for example, it shows that 46 fr-

ames were clustered in the third cluster of cepstrum (se-

cond priority) but not in the third cluster of the autocor-

relation function (first priority). So they were classified

as voiced and according to the table none of them were

misclassified.

5. Disadvantages of the Cepstrum-Based

Voicing Detector

One of the most common methods for extracting pitch

period is to determine the place of the peak in the cep-

stral domain [10]. Furthermore, the cepstral domain in-

formation is used to extract other acoustic parameters.

One of these important parameters is “voicing” which is

extracted based on the peak value in the cepstral domain

by using different methods. But the first problem is that

the peak value in the cepstral domain depends on its

place on the cepstral axis. So when the pitch period gets

larger, the peak value descends with rate

. T he solu-

tion is to multip ly a ramp function in the cepstra l domain.

More details are presented in [20]. More experiments

have shown that the cepstral method has other deficien-

cies. It means that for some voiced frames, although

there is a distinguishable peak in the cepstral domain, it

does not have sufficient value in comparison with the

threshold.

The frames for which cepstrum method cannot per-

form well can be divided into two categories. For each

category a sample frame from TIMIT directory is ana-

lyzed which shows the deficiency of the method obvi-

ously. Note that both autocorrelation and cepstrum me-

thods are used after the energy-normalization of the

frame.

In the categories, in order to prove our claim about all

the frames in the gro up, we mo del the cate gor y with some

known mathematical functions such as “sine” and “sinc”.

The reason that the function can simulate nearly all the

frames in the category is discussed in related sections.

The first category contains the vowels which are

band-limited in the spectral domain. In this case the cep-

stral peak value is small and this leads to wrong v/uv

classification in the cepstrum-based methods. To prove

our claim, we consider a periodic “sinc” function as the

input of the cepstrum method. Note that the spectral peak

values for this input are the same as each other and if we

want to have a spectral shape similar to the frames in this

category, we need to multiply this function with some

formant-like function in the spectral domain. As it is

known, because of the logarithmic property of cepstrum,

this multiplication in the spectral domain will result in

addition in the cepstral domain and since the periodicity

information is in the periodic sinc function, the result of

cepstrum method for the periodic sinc function will be

similar to the result of applying the method to any frame

in thi s category. Figuers 6 and 7 shows the results of this

application to two different sincs, one with limited band-

width and the other with high frequency coefficients.

It can easily be seen that by increasing the bandwidth

of the signal the value of the cepstrum feature has in-

creased from 3.69 to 6.

For more support of our claim we have plotted the

value of the cepstrum feature by increasing the band-

width for a periodic sinc function. The result is depicted

in Figure 8. It can be seen that this value increases as the

bandwidth increases, meaning that the cepstrum performs

better.

The similar results for applying the method to a prac-

tical frame of a vowel (/i:/ like in sheet) are shown in

Figure 9.

The reason can be explained mathematically for a pe-

riodic sinc function (which is an indicator of a band li-

mited signal) as this:

The cepstrum is evaluated from the Equation (2) [10]:

( )( )

{ }

= log

c nsn

−



(2)

As we know if

( )

is a periodic sinc, the fft of its

absolute value will be t he mul tip lication o f a p ulse with a

delta train. Then, its log will also contain some deltas

(the deltas within the pulse width). The larger the sinc’s

BW is (in other words, the sharper the sinc is) the more

deltas will be included in the pulse width (and therefore

more deltas we will have at the output of the fft). Consi-

dering that these deltas show the periodicity of the origi-

nal wa vefo r m (si nc), by i ncre asin g the BW , the o utp ut o f

the ifft (in the cepstrum equation) will have larger value

at the pitch.

A New Method of Voiced/Unvoiced Classification Based on Clustering

344

The second category contains frames related to nasals

such as / n / and / m / which must be labeled voiced in a

correct voicing decision. But the theoretical and practical

results show that their cepstral peak values are so small.

For modeling nasals to study them, we choose a sine

wave, which is a good indicator of nasals.

A theoretical conclusion similar to the one in the first

category can be made here. The results of applying this

explicit waveform can be seen in Figure 10.

The results of applying the method to a practical frame

(/n/ in background) are a l s o shown in Figure 11.

As can be seen for both vowels and nasals, cepstrum

based methods do not perform well to extract the pa-

rameters of the speech segment, such as voicing and

pitch. That's why we do not rely on just one feature. Also

we add a third group for each feature (besides the voiced

and unvoiced groups), so that if that feature is weak in

making the dec i s ion, we can go t hroug h other features.

6. Conclusions

We have presented a new approach of detecting voiced

and unvoiced speech. The main advantage of this clus-

tering-based method is getting rid of determining a thre-

shold. So it is highly speaker independent. Also, the use

of three features has enabled the method to make a better

decision about the segments, in which one feature does

not indicate voicing well. Besides, clustering into three

clusters, or implicitly, double thresholding, helps us to

make the v/uv decision more certainly. Despite the sim-

plicity of the algorithm, the results have shown a satis-

factory performance in comparison with more compli-

cated methods.

number of samples

(a) (b )

number of samples

(c)

Fig ure 6. Testi ng the si nc funct ion for t he first categ ory. (a) A sample sinc function with limited bandwidth; (b) T he spectral

doma in; (c) The cepst ral domain, peak to average = 3.69.

A New Method of Voiced/Unvoiced Classification Based on Clustering

345

number of samples

(a) (b)

number of samples

(c)

Figure 7 . Testing the sinc fu nction for the first c ategory. (a) A sample si nc function w ith high freq uency coeffici ents; (b) The

spectral domain; (c) The cepst ral domain, peak to average = 6 .

number of samples

Figure 8. Cepstral feature for the s inc f unction when incre a sing BW.

A New Method of Voiced/Unvoiced Classification Based on Clustering

346

number of samples

(a) (b)

Figure 9 . Testing a real sp eech fra me f or the first category. (a) The vowel / i:/ as in sheet; (b) The cepstral domain.

number of samples

(a) (b)

Figure 1 0. Testing a sample sine functi o n. (a) The sample sine functi on; (b) The cepstra l do main.

number of samples

(a) (b)

Figure 1 1. Testing a real speech fra me f or the second category. (a) The nasal /n/; (b) The cepstral domain.

A New Method of Voiced/Unvoiced Classification Based on Clustering

347

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