원문정보
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초록
영어
Automatic recognition of spoken words is one of the most challenging tasks in the field of speech recognition. The difficulty of this task is due to the acoustic similarity of many of the words and their syllabi. Accurate recognition requires the system to perform fine phonetic distinctions. This paper presents a technique for recognizing spoken words in Bangla. In this study we first derive feature from spoken words. This paper presents some technique for recognizing spoken words in Bangla. In this work we use MFCC, LPC, GMM and DTW.
목차
Abstract
1. Introduction
2. Objectives
3. Methodology
3.1. Input Speech
3.2. Analog to Digital
3.3. Framing and Overlapping
3.4. Speech Detect
3.5. Pre-emphasis Filter
3.6. Hamming Window
3.7. Fast Fourier Transform
3.8. Feature Extraction
3.9. Matching
3.10. Take Decision
4. Results
4.1. Model 1
4.2. Model 2
4.3. Model 3
4.4. Model 4
4.5. Comparison between Four Models
5. Conclusion
References
1. Introduction
2. Objectives
3. Methodology
3.1. Input Speech
3.2. Analog to Digital
3.3. Framing and Overlapping
3.4. Speech Detect
3.5. Pre-emphasis Filter
3.6. Hamming Window
3.7. Fast Fourier Transform
3.8. Feature Extraction
3.9. Matching
3.10. Take Decision
4. Results
4.1. Model 1
4.2. Model 2
4.3. Model 3
4.4. Model 4
4.5. Comparison between Four Models
5. Conclusion
References
저자정보
참고문헌
자료제공 : 네이버학술정보