Home Articles List Article Information
The Egyptian Journal of Language Engineering
Journal Archive
Volume Volume 11 (2024)
Volume Volume 10 (2023)
Volume Volume 9 (2022)
Volume Volume 8 (2021)
Volume Volume 7 (2020)
Volume Volume 6 (2019)
Volume Volume 5 (2018)
Volume Volume 4 (2017)
Volume Volume 3 (2016)
Volume Volume 2 (2015)
Volume Volume 1 (2014)
Gody, A., Abul Seoud, R., Hassan, M. (2014). Automatic Speech Annotation Using HMM based on Best Tree Encoding (BTE) Feature. The Egyptian Journal of Language Engineering, 1(1), 55-62. doi: 10.21608/ejle.2014.59890
Amr Gody; Rania Abul Seoud; Mohamed Hassan. "Automatic Speech Annotation Using HMM based on Best Tree Encoding (BTE) Feature". The Egyptian Journal of Language Engineering, 1, 1, 2014, 55-62. doi: 10.21608/ejle.2014.59890
Gody, A., Abul Seoud, R., Hassan, M. (2014). 'Automatic Speech Annotation Using HMM based on Best Tree Encoding (BTE) Feature', The Egyptian Journal of Language Engineering, 1(1), pp. 55-62. doi: 10.21608/ejle.2014.59890
Gody, A., Abul Seoud, R., Hassan, M. Automatic Speech Annotation Using HMM based on Best Tree Encoding (BTE) Feature. The Egyptian Journal of Language Engineering, 2014; 1(1): 55-62. doi: 10.21608/ejle.2014.59890

Automatic Speech Annotation Using HMM based on Best Tree Encoding (BTE) Feature

Article 5, Volume 1, Issue 1, January 2014, Page 55-62  XML PDF (499.46 K)
Document Type: Original Article
DOI: 10.21608/ejle.2014.59890
View on SCiNiTO View on SCiNiTO
Authors
Amr Godyorcid ; Rania Abul Seoud email ; Mohamed Hassan
Electrical Engineering Department, Faculty of Engineering, Fayoum University
Abstract
Manual annotation for time-aligning a speech waveform against the corresponding phonetic sequence is a tedious and time consuming task. This paper aimed to introduce a completely automated phone recognition system based on Best Tree Encoding (BTE) 4-point speech feature. BTE is used to find phoneme boundaries along speech utterance. Comparison to Mel-frequency cepstral coefficients (MFCCs) speech feature in solving the same problem is provided. Hidden Markov Model (HMM) and Gaussian Mixtures are used for building the statistical models through this research. HTK software toolkit is utilized for implementation of the model. The System can identify spoken phone at 59.1% recognition rate based on MFCC and 22.92% recognition rate based on BTE. The current BTE vector is 4 components compared to 39 components of MFCC. This makes it very promising features vector, BTE with 4 components gives a comparable recognition success rate compared to the 39 components MFCC vector widely in the area of ASR.
Keywords
BTE; MFCC; HTK; Gaussian Mixture; speech recognition
Statistics
Article View: 179
PDF Download: 539