Discourse Feature Recognition for Text Dynamic Translation

Meng Li

Guangzhou Huali College, Guangzhou, 511325, China

Keywords: Text, Dynamic Translation, Discourse Feature Recognition, Parameter Modulation, Spectrum Feature Extrac-

tion.

Abstract: In order to improve the ability of dynamic translation of foreign media discourse, this paper puts forward a

method of discourse feature recognition and recognition based on spectral density feature decomposition.

Using time-frequency dynamic feature analysis, the dynamic compensation model of foreign media text trans-

lation discourse signal is established, and the noise parameters of foreign media text translation discourse

signal are analyzed and recognized by combining the frequency spectrum density feature estimation method.

The feature decomposition model of foreign media text translation discourse signal is constructed by multi-

frame speech compensation and parameter modulation method, and the wavelet multi-level feature detection

method is adopted. The anti-interference filtering analysis of foreign media text translation discourse is real-

ized. The conditional coding of text translation discourse signal is carried out by the method of spectral density

dynamic feature decomposition, and the statistical information analysis model of foreign media text transla-

tion discourse signal is established. The signal detection and feature recognition of foreign media text trans-

lation discourse signal are realized by text parameter clustering and envelope amplitude-frequency feature

detection. The test results show that the accuracy of foreign media text translation discourse feature recogni-

tion by this method is high, and the output signal-to-noise ratio of the signal is improved by discourse feature

recognition.

1 INTRODUCTION

With the development of foreign media text and dis-

course recognition technology, the accuracy of for-

eign media text translation and discourse recognition

is required higher and higher. A discourse feature

recognition model oriented to dynamic text transla-

tion is established, and a foreign media text transla-

tion discourse signal collection and feature recogni-

tion model is built by combining signal processing

and speech signal analysis methods (John, 2019). The

feature recognition and detection of foreign media

text translation discourse signals are carried out by

combining anti-interference design methods. Im-

prove the ability of signal detection and recognition,

so as to improve the output clarity of foreign media

text translation discourse signals and reduce the inter-

ference of signals. Studying the feature recognition

technology of foreign media text translation discourse

signals plays an important role in the further develop-

ment of foreign media texts (JIA, LAI, YU et al.,

2021).

The feature recognition of foreign media text

translation discourse signal is based on the automatic

test technology of the signal, and adopts the embed-

ded signal processing method combined with signal

feature analysis to carry out anti-interference pro-

cessing and feature analysis of foreign media text

translation discourse signal. Among the current meth-

ods, the feature recognition methods of foreign media

text translation discourse signal mainly include time-

frequency analysis method, statistical feature analysis

method, differential control method and spectral fea-

ture recognition method, etc (AI, YANG, XIONG,

2018). The error gain control model of foreign media

text translation discourse signal is constructed, and

combined with signal gain control, the foreign media

text translation discourse feature recognition pro-

cessing is realized, and the signal interference is re-

duced. However, the traditional method of foreign

media text translation discourse feature recognition

has poor output signal-to-noise ratio and weak adap-

tive control ability (Wang, Li, Meng, 2022).

Aiming at the disadvantages of traditional meth-

ods, this paper puts forward a method of foreign me-

dia text translation discourse feature recognition and

578

Li, M.

Discourse Feature Recognition for Text Dynamic Translation.

DOI: 10.5220/0011752300003607

In Proceedings of the 1st International Conference on Public Management, Digital Economy and Internet Technology (ICPDI 2022), pages 578-583

ISBN: 978-989-758-620-0

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

recognition based on spectral density feature decom-

position. Firstly, the collected foreign media text

translation discourse signals are preprocessed, includ-

ing noise reduction filtering, anti-interference design,

feature extraction, etc., and then the signals are de-

composed by time-frequency decomposition and em-

pirical mode decomposition to realize foreign media

text translation discourse feature recognition. Finally,

the simulation test shows the superior performance of

this method in improving foreign media text transla-

tion discourse feature recognition and recognition

ability.

2 SIGNAL ACQUISITION

DETECTION AND

PREPROCESSING

2.1 Signal Sampling Analysis

In order to realize foreign media text translation dis-

course feature recognition based on spectral density

feature decomposition, firstly, the foreign media text

translation discourse signal is sampled, the time-fre-

quency dynamic feature analysis of foreign media

text translation discourse is adopted, and a dynamic

compensation model of foreign media text translation

discourse signal is established. M omni-directional

signal sources are set to collect foreign media text

translation discourse signal, an expected foreign me-

dia text translation discourse signal and p interference

feature quantitie (Zheng, 2022) s. The collected for-

eign media text translation discourse signals are input

into the TV signal detection system in array, and there

is a foreign media text translation discourse signal

with a length of

, which can be represented as ar-

ray, and it is represented as

()

nR∈

. Through the

encoding and decoding of foreign media text transla-

tion discourse signals, the characteristic marker

model of foreign media text translation discourse sig-

nals is obtained, and the acquisition output of foreign

media text translation discourse signals is expressed

as follows:

=Ψ=Ψ



(1)

Wherein,

is the time domain component of for-

eign media text translation discourse signal,

is the

phase of foreign media text translation discourse sig-

nal detection, and

is the frequency domain com-

ponent of foreign media text translation discourse sig-

nal. The acquisition model of foreign media text

translation discourse signal is constructed, and the

best sampling starting point is obtained:

𝐽

(𝑚)=𝑚𝑎𝑥



{𝐽

(𝜏)+𝐷



(𝜏)+𝐶},

𝐽

(0) = 0

(2)

Wherein,

()J

is the time delay of foreign me-

dia text translation discourse signal detection,

()

is the characteristic decomposition level of foreign

media text translation discourse signal, and

is the

bandwidth of foreign media text translation discourse

signal. According to the constraint characteristics of

the modulation mode of foreign media text translation

discourse signals, the discrete components of broad-

band multi-frame foreign media text translation dis-

course signals after basic decoding are obtained, and

the discrete characteristic equation is expressed as

follows:

() () ()

()( )

() () ()

111

()

|, | ,

(|.)

iii

kkk k kk

xn sn vn

py X Y px X Y

−−−

−

(3)

In the above formula,

()

represents the dis-

crete sequence of foreign media text translation dis-

course signal,

()

represents the color noise com-

ponent,

()

−

represents the scale of foreign media

text translation discourse signal,

()

(|.)

is the prior

probability density of foreign media text translation

discourse signal detection,

()

() ()

kkk

px X Y

−−

is the

conditional probability density of foreign media text

translation discourse signal reliability collection, and

SD is the random probability density component. The

domain feature expression is carried out on the broad-

band multi-frame foreign media text translation dis-

course signal, and the joint parameter identification is

carried out on the foreign media text translation dis-

course signal components in Y to construct the con-

ditional coding model of foreign media text transla-

tion discourse signal. The coding output is as follows:

(|) (|,,)

prl

α= α

∏

(4)

Wherein,

(|,,)

rlα

is the encoded output of for-

eign media text translation discourse frame format,

and L is the sampling sequence length of foreign me-

dia text translation discourse signal. It is assumed that

the foreign media text translation discourse signal is

Discourse Feature Recognition for Text Dynamic Translation

579

decomposed by the adaptive spectrum separation

method, and the sampling model of the foreign media

text translation discourse signal is obtained (Wang,

2021).

2.2 Signal Filtering Pretreatment

By using time-frequency dynamic feature analysis, a

dynamic compensation model of foreign media text

translation discourse signal is established (Lin, 2021),

and the signal anti-interference design is carried out

by combining the estimation method of frequency

spectrum density feature when foreign media text

translation discourse signal is used, and the modula-

tion components of foreign media text translation dis-

course signal are obtained:

𝐸



∑

𝐸

,





(5)

𝑃

,

= 𝐸

,

/𝐸



(6)

Wherein,

is the order of foreign media text

translation discourse signal, and

is the Doppler

frequency offset of foreign media text translation dis-

course. Time-frequency analysis method is used to

filter the foreign media text translation discourse sig-

nal in the interval, and wavelet multi-level feature de-

tector is used to obtain the modulation frequency

spectrum

of foreign media text translation dis-

course signal as follows:

𝑊𝐸



= −

∑

𝑃

,

𝑙𝑛( 𝑃

,

) (7)

Wherein,

is the carrier frequency of foreign

media text translation discourse signal, and

ln( )

is the amplitude of filtering detection. The interfer-

ence suppression component is determined by the or-

der determination algorithm, and the modulation pro-

cessing of the broadband multi-frame foreign media

text translation discourse signal is carried out. The in-

put characteristic sequence of the broadband multi-

frame foreign media text translation discourse signal

(0),..., ( 1)xxN−x=[ ]

, which is a discrete se-

quence of the finite-length broadband multi-frame

foreign media text translation discourse signal.

01nN≤≤ −

D, the empirical mode decomposi-

tion method is adopted to filter and detect the broad-

band multi-frame foreign media text translation dis-

course signal, and the average sum of the filtered out-

put of foreign media text translation discourse is ob-

tained.

𝑆



= 𝐸



𝑥



(𝑡)



√

𝑠𝑏 (8)

422

() 3 ()

Ex t E x t b

 

=−

 

(9)

Wherein, 𝐸



𝑥



(𝑡)



is the passband of foreign me-

dia text translation discourse signal transmission,

represents multi-frame information gain,

()Ext





is the modulation frequency offset of foreign media

text translation discourse, and

is the joint probabil-

ity density distribution. According to the filtering re-

sults, the multi-frame speech compensation and pa-

rameter modulation method are used to construct the

feature decomposition model of foreign media text

translation discourse signal, which can improve the

ability of discourse feature recognition and pro-

cessing (ABDUL RAUF, SCHWENK, 2011).

3 DISCOURSE FEATURE

RECOGNITION AND

PROCESSING

3.1 Empirical Mode Decomposition of

Signal

The method of multi-frame speech compensation and

parameter modulation is used to construct the feature

decomposition model of foreign media text transla-

tion discourse signal, and the method of spatial pa-

rameter recognition is used to construct the ambiguity

detection model of foreign media text translation dis-

course signal (MARIE, FUJITA, 2017). After the in-

terference position is determined and zeroed, the dy-

namic amplitude feature recognition model of broad-

band multi-frame foreign media text translation dis-

course signal is obtained. The joint decision function

is:



𝐻



: 𝑥'(𝑡)=𝑤(𝑡)

𝐻



√

𝐸𝑠'(𝑡)+𝑤(𝑡)

0 ≤𝑡≤𝑇 (10)

In the formula, x'(t) and s'(t) are:

𝑥'(𝑡)=𝑥(𝑡)*ℎ



(𝑡) (11)

𝑠'(𝑡)=𝑠(𝑡)*ℎ



(𝑡) (12)

Wherein,

()wt

is the single-line spectrum of for-

eign media text translation discourse signal,

is the

time sampling interval,

is the distributed energy

spectrum of foreign media text translation discourse,

𝑥'(𝑡) is the convolution of foreign media text transla-

ICPDI 2022 - International Conference on Public Management, Digital Economy and Internet Technology

580

tion discourse signal, ℎ



(𝑡) is the filter transfer func-

tion of foreign media text translation discourse signal,

the M-order phase polynomial is determined, and the

improved empirical mode decomposition method is

adopted. The scale decomposition coefficient of the

broadband multi-frame foreign media text translation

discourse signal is

(1) ( ) ( 1)

, , 2

NNNN jJ

−

==≤≤

, and

each sub-signal is described by

∑

𝑃



=1, so the out-

put probability density characteristic quantity of the

broadband multi-frame foreign media text translation

discourse signal satisfies. The energy set of the broad-

band multi-frame foreign media text translation dis-

course signal is described by {𝑃



, 𝑃



,...,𝑃



}, and the

single-line spread characteristic decomposition

method is adopted. The residual frequency offset of

the broadband multi-frame foreign media text trans-

lation discourse signal after discrete orthogonal

wavelet transform is obtained, and {𝑃



, 𝑃



,...,𝑃



} is

used to represent the finite time sequence of the

broadband multi-frame foreign media text translation

discourse signal, that is, the discrete sequence output

by empirical mode decomposition of the foreign me-

dia text translation discourse signal is:

() ( )

[0,..., 1]XXN=−X

(13)

Wherein,

is the discrete sequence length of

foreign media text translation discourse signal. Ac-

cording to the empirical mode decomposition of for-

eign media text translation discourse signal, the fre-

quency parameters of foreign media text translation

discourse signal are estimated as follows:

()

nian

−



(14)

Wherein, 𝑖 is the sampling sequence point of for-

eign media text translation discourse signal, and 𝑎



the sampling track of foreign media text translation

discourse signal, 𝑛



is Z transform. At the receiving

end, through information output identification, the

time domain discrete components of foreign media

text translation discourse signals are expressed as fol-

lows:

{

}

,,,

xx x= 

(15)

Thus, the discrete components of the signal are

obtained by empirical mode decomposition. Accord-

ing to the constraint feature quantity of foreign media

text translation discourse signal modulation mode, the

signal discrete sequence of foreign media text trans-

lation discourse signal modulation is obtained, and

the feature decomposition model of foreign media

text translation discourse signal is constructed by

multi-frame speech compensation and parameter

modulation method, so as to improve the feature

recognition ability of the signal (ZHANG, LI, YANG

et al., 2020).

3.2 Realization Of Discourse Feature

Recognition

The amplitude of modulation mode of foreign media

text translation discourse signal obtained by fre-

quency domain parameter identification is:

() ( , ) arg[ ( )]

d dadb

yt ab Z f

=−



(16)

In the above formula, 𝑓(𝑡) is the estimated value

of modulation parameters of foreign media text trans-

lation discourse signal, 𝜌(𝑎, 𝑏) is the ambiguity of

foreign media text translation discourse signal, and

is the broadband multi-frame length of foreign media

text translation discourse signal. The modulation

model of foreign media text translation discourse sig-

nal is constructed, and the modulation output expres-

sion is as follows:

()

() ( )+( )

Ku Z f Af e

(17)

Wherein,

𝑍(𝑓) represents the real part of foreign

media text translation discourse signal,

𝐴(𝑓) repre-

sents the i333maginary part, and

𝑒

()

represents the

frequency offset. According to the foreign media text

translation discourse signal modulation processing,

the output expression of discourse feature recognition

can be expressed as:

() ( )

jft

Yu S f e df

∞

−∞



(18)

Wherein,

𝑆(𝑓) represents the energy spectrum

component of foreign media text translation dis-

course, and

𝑓 represents the modulation frequency of

foreign media text translation discourse. The steep

gradient of modulation of foreign media text transla-

tion discourse signal is calculated. Based on the intel-

ligent recognition method, the conditional coding of

foreign media text translation discourse signal is car-

ried out, and the statistical information analysis

model of foreign media text translation discourse sig-

nal is established. Through text parameter clustering

and envelope amplitude-frequency feature detection,

the signal detection and feature recognition of foreign

media text translation discourse signal are realized.

Discourse Feature Recognition for Text Dynamic Translation

581

4 SIMULATION TEST

In order to verify the application performance of this

method in foreign media text translation discourse

feature recognition, Matlab is used for experimental

analysis, and the sample number of foreign media text

translation discourse signal is set to 20, the signal

frame length is 2000~4000, and the chip length is

4016. See Table 1 for the initial parameter settings of

the signal.

Table 1: Initial parameter setting of signal.

Signal

sequence

Bandwidth

/Kbps

Interference

intensity

/dB

Sampling

frequency

/KHz

Sample 1 12.034 3.772 21.000

Sample 2 12.592 3.476 21.516

Sample 3 12.695 3.592 21.370

Sample 4 12.016 3.194 21.886

Sample 5 12.439 3.310 21.262

Sample 6 12.548 3.468 21.982

Sample 7 12.316 3.014 21.372

Sample 8 12.537 3.462 21.695

Sample 9 12.184 3.249 21.661

Sample 10 12.525 3.649 21.775

Sample 11 12.168 3.052 21.585

Sample 12 12.761 3.486 21.181

Sample 13 12.339 3.479 21.558

Sample 14 12.538 3.696 21.358

Sample 15 12.209 3.502 21.312

Sample 16 12.088 2.981 21.577

Sample 17 12.674 3.066 14.176

Sample 18 12.034 3.772 21.000

Sample 19 12.592 3.476 21.516

Sample 20 12.695 3.592 21.370

Figure 1: Original signal.

According to the parameter settings in Table 1,

foreign media text translation discourse feature

recognition processing is performed, and the original

signal is shown in Fig 1, and the signal after feature

recognition is shown in Fig 2.

Figure 2: Discourse feature recognition.

By comparing the results of Fig 1 and Fig 2, it can

be seen that the peak gain of the foreign media text

translation speech signal is obviously expressed by

this method, which indicates that the foreign media

text translation speech signal has strong anti-interfer-

ence ability, and the output signal-to-noise ratio

(SNR) is tested, and the comparison result is shown

in Fig 3. By analyzing the results of Fig 3, it can be

seen that the output signal-to-noise ratio (SNR) of the

foreign media text translation speech signal is higher

than that of the traditional method.

Figure 3: Contrast test of output signal-to-noise ratio.

ICPDI 2022 - International Conference on Public Management, Digital Economy and Internet Technology

582

5 CONCLUSIONS

In this paper, a model of foreign media text transla-

tion discourse signal collection and feature recogni-

tion is constructed, and the feature recognition and

detection of foreign media text translation discourse

signal are carried out by combining anti-interference

design methods, so as to improve the ability of signal

detection and recognition, and thus improve the abil-

ity of foreign media text translation discourse feature

recognition. In this paper, a method of speech feature

recognition and recognition of foreign media text

translation based on spectral density feature decom-

position is proposed. Based on the intelligent recog-

nition method, the conditional coding of text transla-

tion speech signal is carried out, the statistical infor-

mation analysis model of foreign media text transla-

tion speech signal is established, and the speech fea-

ture recognition is realized by combining signal fil-

tering and detection recognition. The test shows that

the output signal-to-noise ratio (SNR) of the feature

recognition processing of multi-frame foreign media

texts is high and the feature recognition effect is good.

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