o l . 7, i 3, ju l y - se p t 2016 issn : 2230-7109 ... · fig. 1.2: the block diagram shows the...

IJECT Vol. 7, IssuE 3, July - sEpT 2016 ISSN : 2230-7109 (Online) | ISSN : 2230-9543 (Print)

w w w . i j e c t . o r g 56 INterNatIONal JOurNal Of electrONIcS & cOmmuNIcatION techNOlOgy

Model for Optimization of Speech Recognition and Performance Analysis1Dr .Kadam Vipulsangram K, 2Zine Jyoti P

1,2Dept of Electronics Engg., P.E.S College of Engg., Nagsenvan, Dr. Babasaheb Ambedker Marathwada University, Aurangabad, MS, India

AbstractOur research work explore the possibility of the optimization of speech recognition tool. By introduction of BP Digital filter at the input section of LPC section from which we receive slight enhancement in the Mainlobe Width from 1.4025 dB to 1.5016 dB with Sidelobe Attenuation as 1.5 dB. This implies the rise & optimization of 7.06 % power of desired speech signals. Then we have tried for LPC Tuning (varying the parameters of emphasis & de-emphasis digital Filter) For the numerator coefficients as 1, .9999 for emphasis & 1.9000 for de-emphasis filter we observe that there is again increase in the Mainlobe Width as 1.7269 dB. This implies the rise & optimization of 23.13 % power of desired speech signals.Finaly we varying the order of the filter in this we have design the 40 order filter which gives the mainlobe width as 2 dB which gives the more enhancement the Width of Mainlobe. This implies the rise & optimization of 42.60 % power of desired speech signals.

KeywordsLPC, Mainlobe, MFCC, OTSR

1.1. MethodologyThe work is focused on designing of Optimization Tool for Speech Recognition (OTSR) by applying some modifications in the basic model of LPC & with the addition of DSP filters , further to implement on a DSP hardware which will give the better performance in noisy environment with high efficiency for Digital Transmission.

1.2. Contribution of this Model

1.2.1. Simulink ModelThe block diagram below shows the system we implement

Figure 1.1shows block diagram of methodology used to design of OTSR.

Digital Filter

Pre-Emphasis

Overlap Analysis Window,

Autocorrelation, Levinson -Durbin

Time Varying

Analyzing Filter

Time Varying

Synthesis Filter

De-Emphasis

Digital Filter

LPC OUT Re-Synthesized

Signal

Reflection Coefficients

Digital Filter

Imported speech samples from this PC

Fig. 1.1: Model for Development of OTSR

P a d

ha mming

Window

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R eflectionC oeffs

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Digita lF ilter

P re-E mpha sis

Welch

P eriodogra m1

Welch

P eriodogra m

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L evinson-Durbin

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L P CS pectrum

F 10.mp3A: 8000 Hz, 16 bit, mono

Audio

F rom Multimedia F ile

F F T

Displa y1Displa y

F DAT ool

Digita lF ilter Design

Digita lF ilter

De-E mpha sis

AC F

Autocorrela tion

Fig. 1.2: The block diagram shows the system implement using software

Here you implement a speech compression technique known as Linear Prediction Coding (LPC) using DSP System Toolbox™ functionality available at the MATLAB® command line.

1.3. Comparison Results & Performance AnalysisFollowing are the three main contribution results of our system

1. Introduction of BP Digital Filter at the Input Section Here we have introduce the Band Pass digital filter with the sampling frequency of 20 KHz, lower cutoff as 60 Hz,10 order & higher cutoff as 8 KHz. From the table no.1.5 which shows the average of width considering the 80 speech samples of male & female students, in this we have receive slight enhancement in the Mainlobe Width (-3dB) from 1.2723 to 1.3674 with Sidelobe Attenuation as 1.5 dB. This implies the rise & optimization of 7 % power of desired speech signals as per the mainlobe is concern. Also from table no.1.6 which shows the average signal to noise ratio considering 80 speechsample signals of male & female students, in this we achieve the enhancement in signal to noise ratio from 0.0024 dB to 2.1123 dB.This implies the rise & optimization of 91 % power of desired speech signals as per the signal to noise is concern.

2. LPC Tuning (Varying the Parameters of Emphasis & De-Emphasis Digital Filter)For the numerator coefficients as 1, .9999 for emphasis & 1.9000 for de-emphasis filter From the table no.1.5 which shows the average of width considering the 80 speech samples of male & female students, in this we have receive slight enhancement in the Mainlobe Width (-3dB) from 1.3674 to 1.5926. This implies the rise & optimization of 15 % power of desired speech signals as per the mainlobe is concern. Also from table no.1.6 which shows the average signal to noise ratio considering 80 speechsample signals of male & female students, in this we achieve the enhancement in signal to noise ratio from 2.1123 dB to 2.3017 dB.This implies the rise & optimization of 95 % power of desired speech signals as per the signal to noise is concern.

IJECT Vol. 7, IssuE 3, July - sEpT 2016

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ISSN : 2230-7109 (Online) | ISSN : 2230-9543 (Print)

C. Varying the Order of the Filter In this we have design the 40 order filter.From the table no.4.5 which shows the average of width considering the 80 speech samples of male & female students, in this we have receive slight enhancement in the Mainlobe Width (-3dB) from 1.5926 to 1.6641. This implies the rise & optimization of 35 % power of desired speech signals as per the mainlobe is concern. Also from table no.1.6 which shows the average signal to noise ratio considering 80 speechsample signals of male & female students, in this we achieve the enhancement in signal to noise ratio from 2.3017 dB to 3.4898 dB.This implies the rise & optimization of 98 % power of desired speech signals as per the signal to noise is concern.Following tables shows the comparison of the result achieved with the available model in terms of mainlobe width (-3dB) & Signal to Noise ratio in dB.

Table 1.1: Comparison of Mainlobe width (-3dB) for male samplesSamples Mainlobe width (-3dB)

Male MFCC LPC Contribution-1 Contribution-2 Contribution-3M1 0.4155 1.4025 1.5016 1.8462 1.9999M2 0.5231 1.6531 1.6543 1.7257 1.6924M3 0.7121 1.9229 1.8485 1.9999 1.6717M4 0.6521 1.7935 1.7949 1.7908 1.6730M5 0.4156 1.4027 1.5748 1.8948 1.7256M6 0.0001 0.0103 0.0103 1.7859 1.9999M7 0.3310 1.2395 1.5155 1.6546 1.7635M8 0.0006 0.0005 0.0005 1.7598 1.7628M9 0.4181 1.6531 1.7903 1.7906 1.7114

Table 1.2: Comparison of Mainlobe Width (-3dB) for Female SamplesSamples Mainlobe width (-3dB)

Male MFCC LPC Contribution-1 Contribution-2 Contribution-3F1 0.0002 0.0006 0.0006 1.7584 1.9999F2 0.3110 1.5154 1.6532 1.6534 1.6602F3 0.0002 0.0005 0.0005 1.4634 1.9999F4 0.4010 1.6778 1.7563 1.6561 1.7330F5 0.4110 1.6529 1.8908 1.7905 1.7133F6 0.0002 0.0006 0.0006 1.6047 1.7324F7 0.0002 0.0006 0.0006 1.8298 1.7267F8 0.0110 0.5327 1.1214 1.5942 1.6232F9 0.4189 1.6533 1.7350 1.6572 1.6685

Table 1.3: Comparison of Signal to Noise Ratio in dB for Male SamplesSamples Signal - to - Noise Ratio in dB

Male MFCC LPC Contribution-1 Contribution-2 Contribution-3M1 -3.1785 0.0000 3.0485 5.2133 5.4673M2 -4.9349 0.0100 3.6278 3.7456 8.2772M3 -0.8186 0.0010 2.6781 2.7654 4.7865M4 -3.7571 0.0000 4.4457 4.4420 5.9872M5 -3.8711 0.0020 5.1331 5.2017 6.9831M6 -0.4010 0.0010 2.8912 2.8903 4.0987M7 -3.9663 0.0010 6.1280 6.2100 7.8765M8 -4.8017 0.0010 3.8478 3.8502 4.8976M9 -1.1861 0.0100 6.4361 6.6994 7.2347



Table 1.4: Comparison of Signal to Noise Ratio in dB for Female Samples

Samples Signal - to - Noise Ratio in dB

Male MFCC LPC Contribution-1 Contribution-2 Contribution-3

F1 -0.4488 0.0000 1.2983 1.3092 2.8732

F2 -0.5502 0.0000 2.8631 2.9834 3.9821

F3 -0.4628 0.0010 2.1020 2.1923 3.9823

F4 -3.8711 0.0001 3.4297 3.5423 3.6681

F5 -0.4493 0.0001 3.9012 3.9834 4.9237

F6 -1.6171 0.0010 2.7634 2.8734 3.8721

F7 -1.7907 0.0001 3.9619 3.9926 4.7325

F8 -0.5012 0.0011 1.2398 1.2399 2.9831

F9 -3.0016 0.0010 1.1674 5.6869 6.5421

Table 1.5: Average of Mainlobe Width (-3dB) of Above SamplesAverage of Mainlobe width(-3dB) of above samples

MFCC LPC Contribution-1 Contribution-2 Contribution-30.3987 1.2723 1.3674 1.5926 1.6641

Table 1.6: Average of Signal-to-Noise Ratio in dB of Above SamplesAverage Signal - to - Noise Ratio in dB

MFCC LPC Contribution-1 Contribution-2 Contribution-3-2.0574 0.0024 2.1123 2.3017 3.4898

Above table shows the comparison results achieved with available model

Following figure gives the performance analysis of available MFCC system using software.

Fig. 1.3: Result of Available Model Using MFCC.




Following figure gives the performance analysis of available LPC system using software.

Fig. 1.4: Result of Available Model Using LPCFollowing figures shows the performance of our contribution.

Fig. 1.5: Contribution-1: Result of Model with introduction of Digital filter at input section.

Fig. 1.6: Contribution-2: Result of Model with LPC Tuning (varying the parameters of emphasis & de-emphasis digital Filter).



Fig. 1.7: Contribution-3: Result of Model with varying the order of Digital Filter.

2.1. Performance Analysis of Optimization Tool for Speech Recognition using LPC & Matlab with Simulink environment:In this we have tried to give the analysis using LPC Technic with Matlab & Simulink environment. We have received the following results as shown in following system block diagrams& output waveforms.

Fig. 2.1: LPC Spectrum using Matlab & Simulink

Fig. 2.2: Reflection Coefficients using Matlab & Simulin

Fig. 2.3: Residual using Matlab & Simulink

Fig. 2.4: Signal & LPC Spectrum using Matlab & Simulink

2.2. Reduction of NoiseFollowing fig shows the spectrum of the output without & with Digital Filter:




AmplitudeFig. 2.5: Result without Digital Filter

Fig. 2.6: Result with Digital Filter

3. Concluding RemarksWe have seen here the implementation of system using software. The implementation used the DSP System Toolbox functionality available at the MATLAB command line. We have compare the achieved result with the existing models MFCC & LPC. From the comparison table we have achieved the rise & optimization of 7 % power considering the mainlobe & 91% power considering signal to noise ratioof desired speech signals in contribution 1,rise & optimization of 15 % power considering mainlobe & 95% power considering signal to noise ratioof desired speech signals in contribution 2, rise & optimization of 35 % power considering mainlobe & 98% power considering signal to noise ratioof desired speech signals in contribution 3. Also we have compare the result considering the 80 samples of male & female.

IV. ConclusionWe develop our OTSR system which will be operate in noise environment because human beings are able to recognize speech amazingly well in high levels of background noise. On the other hand, the performance of Automatic Speech Recognition (ASR) systems degrades dramatically with increasing noise. Part of the reason for this difference lies in the fact that the auditory system incorporates several features that make it more robust to noise. Finally we conclude with the following points-

We have implemented both software as well as hardware 1. model for our OTSR & we achieve the optimization of speech recognition system.By using Matlab & Simulink we implement the Software 2. Model whereas the hardware Model is implemented using

Matlab, Simulink, and Code Composer Studio & Hardware TMS3206711/6713 from Texas Instruments. In both implementations we have achieved the same result which reflects the stability of our system. Introduction of BP Digital filter at the input section 3.

Here we have introduce the Band Pass digital filter with the sampling frequency of 20 KHz, lower cutoff as 60 Hz,10 order & higher cutoff as 8 KHz. From the table no.4.5 which shows the average of width considering the 80 speech samples of male & female students, in this we have receive slight enhancement in the Mainlobe Width (-3dB) from 1.2723 to 1.3674 with Sidelobe Attenuation as 1.5 dB. This implies the rise & optimization of 7 % power of desired speech signals as per the mainlobe is concern. Also from table no.4.6 which shows the average signal to noise ratio considering 80 speech sample signals of male & female students, in this we achieve the enhancement in signal to noise ratio from 0.0024 dB to 2.1123 dB. This implies the rise & optimization of 91 % power of desired speech signals as per the signal to noise is concern.LPC Tuning (varying the parameters of emphasis & de-4. emphasis digital Filter)

For the numerator coefficients as 1, .9999 for emphasis & 1.9000 for de-emphasis filter From the table no.4.5 which shows the average of width considering the 80 speech samples of male & female students, in this we have receive slight enhancement in the Mainlobe Width (-3dB) from 1.3674 to 1.5926. This implies the rise & optimization of 15 % power of desired speech signals as per the mainlobe is concern. Also from table no.4.6 which shows the average signal to noise ratio considering 80 speech sample signals of male & female students, in this we achieve the enhancement in signal to noise ratio from 2.1123 dB to 2.3017 dB.This implies the rise & optimization of 95 % power of desired speech signals as per the signal to noise is concern.Varying the order of the filter 5.

In this we have design the 40 order filter. From the table no.4.5 which shows the average of width considering the 80 speech samples of male & female students, in this we have receive slight enhancement in the Mainlobe Width (-3dB) from 1.5926 to 1.6641. This implies the rise & optimization of 35 % power of desired speech signals as per the mainlobe is concern. Also from table no.4.6 which shows the average signal to noise ratio considering 80 speech sample signals of male & female students, in this we achieve the enhancement in signal to noise ratio from 2.3017 dB to 3.4898 dB.This implies the rise & optimization of 98 % power of desired speech signals as per the signal to noise is concern. Introduction of BP Digital filter at the input section.

V. Directions for Future workBased on the work presented in this thesis the directions for future research work couldbe –

The current system can be improved which will accept input both audio as well as video signals. This can be achieved by using advance processors from Texas Instrument considering LPC section also. Work can be carried out on real time images, Video Signal, Audio Signals obtained from the satellite.Most robustsystems can be developed which can be used in forensic laboratory.



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[5] Bhattacharya, S.; Singh, S.K.; Abhinav, T,“Performance evaluation of LPC and cepstral speech coder in simulation and inreal-time”, Recent Advances in Information Technology (RAIT), 2012 1st International Conference on Digital Object Identifier:10.1109/RAIT.2012.6194531 Publication Year: 2012, pp. 826 - 831

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