The document presents a methodology for isolated Bangla word recognition and speaker detection using a Semantic Modular Time Delay Neural Network (MTDNN). The authors collected a dataset of 690 audio samples from 5 speakers uttering 46 Bangla words. They used the MTDNN framework to classify words and identify speakers with an accuracy of 82.66%. This represents an improvement over previous work on Bangla speech recognition. The authors conclude the MTDNN approach is effective for Bangla word recognition and speaker detection but recommend expanding the dataset and vocabulary in future work.

1. Isolated Bangla Word Recognition and Speaker Detection by
Semantic Modular Time Delay Neural Network (MTDNN)
Authors:
Md. Yasin Ali Khan, S. M. Mostaq Hossain and Mohammed Moshiul Hoque
Department of Computer Science and Engineering, Chittagong University of Engineering
and Technology, Chittagong-4349, Bangladesh
Presented by:
Md. Yasin Ali Khan

2. Introduction
• Speech is the most common forms of human
communication.
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• Also used for human to machine communication.
Human to human Human to machine

3. Word recognition: Recognizing an uttered word that
matched with the actual word.
Speaker recognition: Recognizing a particular person based
on his/her voice signal.
Isolated words: Adjacent words having sufficient silence.
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Introduction (cont.)

4. 3 May 2016 4
Sp-1
Sp-2
Sp-3
আকাশ, পানি, িদী
পানি MTDNN Module
Modular Time Delay Neural Network (MTDNN): Development
of a time-delay neural network which is scaled by modularity of
large nets and based on smaller subcomponent nets.
Figure 1: Abstract representation of proposed framework.
Introduction (cont.)

5. 3 May 2016 5
• No previous work on speaker detection in Bangla
language
• Experimenting with large vocabulary of Bangla
words and different syllables
Motivation

6. • Bangla speech based data documentation & information retrieval
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Control
module
• Bangla speech based security control
Control
module
Motivation (cont.)

7. • Designed a theoretical framework for recognizing Bangla
isolated words and detecting corresponding speakers.
• Implemented the framework using Modular Time Delay
Neural Network (MTDNN).
• Evaluated the system by using different Bangla words and
speakers.
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Contributions

8. • Speech Recognition using Artificial Neural Network (Roy et al. , 2002)
- Restricted on 5 words
- ANN Technology
- FFT Feature
• Speech Recognition using 3 Layer Back Propagation Neural Network (Islam
et al. , 2005)
- Restricted on 30 words
- Develops Multi-Layer Back-propagation NN
- FFT Feature
Limitation: High training time
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Bangla Language
Related Work

9. 3 May 2016 9
Methodology
• Problem Formulation
𝑣 𝑡 =
𝐸ₒ𝑒ᵅᵗ sin 𝜔𝑡 0 ≤ 𝑡 < 𝑇𝑒
𝐸₁ 𝑒¯ᵝ⁽ᵗ¯ᵀᵉ⁾ − 𝑒¯ᵝ⁽ᵀᵉ¯ᵀ ͨ⁾ 𝑇𝑒 ≤ 𝑡 < 𝑇𝑐
0 𝑇𝑐 ≤ 𝑡 ≤ 𝑇𝑜
Glottal Pulse Model for Voiced Signals as The Liljencrants-
Fant (LF) model [5]

10. 3 May 2016 10
Methodology (cont.)
• Mathematical Description
[ŵ₁, ŵ₂,….ŵɴ] = 𝒎𝒂𝒙
𝒘 𝟏, 𝒘 𝟐 ,…….𝒘 𝑵
𝒇( 𝒘 𝟏, 𝒘 𝟐 , … … . 𝒘 𝑵 𝑿, 𝜦)
[P₁, P₂,….Pɴ] = 𝒎𝒂𝒙
𝒑 𝟏, 𝒑 𝟐 ,…….𝒑 𝑵
𝒇( 𝒑 𝟏, 𝒑 𝟐 , … … . 𝒑 𝑵 𝑿, 𝜦)
- For Speech Recognition
- For Speaker Detection
where f is the conditional probability of a sequence of words W given
a sequence of speech features X and 𝚲 is pre-trained speech model.
where f is the conditional probability of a sequence of speakers P
given a sequence of speaker features X and 𝚲 is pre-trained speaker
model.

11. 3 May 2016 12Figure 2: System Architecture.
Methodology (cont.)

12. 3 May 2016 13
• MTDNN
Framework
Figure 3: MTDNN framework.
Methodology (cont.)

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• Environmental Setup
Operating System Lab in CUET
• System Requirements
Operating System : Windows 7 (Core i3, 3.66 GHz, 8GB RAM)
Software : Creative WaveStudio, Matlab
Tools : Close talk microphone
Experiments

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• Data Collection
Experiments (cont.)
No. of Participants : 5
No. of Words : 46
Age Difference : 21-23
Uttering times : 3 (For each word)
(5 [participants] ˟ 46 [no. of words] ˟ 3 [no. of times uttered each word])
= 690 samples
• Sample Size

15. 3 May 2016 15
Average file size 81.67 KB
Total no files 690
Average duration
of files (.wav)
0.82 sec
Age average 22
• File Information
for Samples
Experiments (cont.)

16. 3 May 2016 16
Experiments (cont.)
Mono Syllable:
দেশ, বই, দবশ, দশোক, দ োট, কোজ, তোর, রোত, গোছ, দকউ, আজ
Di Syllable:
তততি, পড়েি, হড়ব, টোকো, থোড়কি, আমরো, পোতো, পুতিশ, কথো, উৎসব, যুদ্ধ, তবজয়
Tri Syllable:
ধরড়ের, জোিোড়িোর, আমোড়ের, বতত মোি, ফিোফি, সুতবধো, কততত পড়ের, দসৌন্দযত, পেতযোগ, পততথবীর
Poly Syllable:
স্বোধীিতোর, সহড়যোতগতো
Poly Syllable:
Word, file, open, print, exit, edit, cut, copy, paste, doc1, doc2
• Sample Words List

17. 3 May 2016 17
Epoch 3
Avg. training time 1 minute and 5 seconds
Gradient error 4.69e-13 [Range (1.00 to 1.00e-10)]
Mu error 1.00e-06 [Range (0.00100 to 1.00e+10)]
Validation
checks
2
Performance 0.165 [Range (0.200 to 0.00)]
Accuracy 82.66%
• Result Analysis of
Word Recognition
&
Speaker Detection
Experimental Results

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Method No of words Feature Recognition
rate
Roy et al. [1] 5 FFT 82%
Islam et al. [2] 13 FFT 78.85%
Proposed 35 MFCC 82.66%
• Comparison Table with
Previous Work
Experimental Results (cont.)

19. • Technology: Modular Time Delay Neural Network
(MTDNN)
• 82.66% overall accuracy ( in avg.)
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Conclusion

20. 3 May 2016 20
• Future Recommendations
• Experimenting with More Bangla Words
• Variable Age of Participants
Conclusion (cont.)

21. [1] K. Roy, D. Das, and M. G. Ali, “Development of the speech recognition system using artificial neural
network,” in Proc.5th International Conference on Computer and Information Technology (ICCIT02),
Dhaka, Bangladesh, 2002.
[2] M. R. Islam, A. Sayeed, M. Sohail, M. W. H. Sadid, M. A. Mottalib, “Bangla Speech Recognition
using three layer Back-Propagation Neural Network”, Proc. of NCCPB, Dhaka, 2005.
[3] M. F. Khan and R. C. Debnath, “Comparative Study of Feature ExtractionMethods for Bangla
Phoneme Recognition”, 5th ICCIT-2002, Dhaka, Bangladesh, pp. 257-261.
[4] J. C. Bezdek, R. Ehrlich, W. Full, “FCM: The Fuzzy C-Means Clustering Algorithm”, Computers &
Geosciences Vol. 10, No. 2-3, pp. 191-203, 1984.
[5] L. R. Rabiner and B. H. Juang, “Fundamentals of Speech Recognition”, Prentice-Hall, Englewood
Cliffs, NJ, 1993.
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References

22. Thank You !!!