Investigation of ANFIS and FFBNN Recognition Methods Performance in Tamil Speech Word Recognition

2014 ◽  
Vol 2 (2) ◽  
pp. 43-53 ◽  
Author(s):  
S. Rojathai ◽  
M. Venkatesulu
Keyword(s):  
Feature Extraction ◽  
Word Recognition ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Back Propagation ◽  
Recognition System ◽  
Inference System ◽  
Feed Forward Back Propagation ◽  
Statistical Measures ◽  
Recognition Systems

In speech word recognition systems, feature extraction and recognition plays a most significant role. More number of feature extraction and recognition methods are available in the existing speech word recognition systems. In most recent Tamil speech word recognition system has given high speech word recognition performance with PAC-ANFIS compared to the earlier Tamil speech word recognition systems. So the investigation of speech word recognition by various recognition methods is needed to prove their performance in the speech word recognition. This paper presents the investigation process with well known Artificial Intelligence method as Feed Forward Back Propagation Neural Network (FFBNN) and Adaptive Neuro Fuzzy Inference System (ANFIS). The Tamil speech word recognition system with PAC-FFBNN performance is analyzed in terms of statistical measures and Word Recognition Rate (WRR) and compared with PAC-ANFIS and other existing Tamil speech word recognition systems.

Computer-aided teaching mode of oral English intelligent learning based on speech recognition and network assistance

2020 ◽  
Vol 39 (4) ◽  
pp. 5749-5760
Author(s):  
Yanfei Hai
Keyword(s):  
Recognition Performance ◽  
Recognition Rate ◽  
Recognition System ◽  
Prosodic Features ◽  
Simulation Experiments ◽  
Teaching Mode ◽  
Speech Detection ◽  
Rate Intensity ◽  
Recognition Systems ◽  
Better Than

The purpose of this paper is to use English specific syllables and prosodic features in spoken speech data to carry out English spoken recognition, and to explore effective methods for the design and application of English speech detection and automatic recognition systems. The method proposed by this study is a combination of SVM_FF based classifier, SVM_IER based classifier and syllable classifier. Compared with the method based on the combination of other phonological characteristics such as phonological rate, intensity, formant and energy statistics and pronunciation rate, and the syllable-based classifier based on specific syllable training, a better recognition rate is obtained. In addition, this study conducts simulation experiments on the proposed English recognition and identification method based on specific syllables and prosodic features and analyzes the experimental results. The result found that the recognition performance of the English spoken recognition system constructed by this study is significantly better than the traditional model.

Phase Autocorrelation Bark Wavelet Transform (PACWT) Features for Robust Speech Recognition

2015 ◽  
Vol 40 (1) ◽  
pp. 25-31 ◽  
Author(s):  
Sayf A. Majeed ◽  
Hafizah Husain ◽  
Salina A. Samad
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Speech Recognition ◽  
Extraction Method ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Feature Extraction Method ◽  
Female Data ◽  
Recognition Systems ◽  
New Feature

Abstract In this paper, a new feature-extraction method is proposed to achieve robustness of speech recognition systems. This method combines the benefits of phase autocorrelation (PAC) with bark wavelet transform. PAC uses the angle to measure correlation instead of the traditional autocorrelation measure, whereas the bark wavelet transform is a special type of wavelet transform that is particularly designed for speech signals. The extracted features from this combined method are called phase autocorrelation bark wavelet transform (PACWT) features. The speech recognition performance of the PACWT features is evaluated and compared to the conventional feature extraction method mel frequency cepstrum coefficients (MFCC) using TI-Digits database under different types of noise and noise levels. This database has been divided into male and female data. The result shows that the word recognition rate using the PACWT features for noisy male data (white noise at 0 dB SNR) is 60%, whereas it is 41.35% for the MFCC features under identical conditions

A Study of Different Methodologies Helpful in the Identification of Offline Handwritten Script

2018 ◽  
Vol 6 (6) ◽  
pp. 307
Author(s):  
Manish M. Kayasth ◽  
Bharat C. Patel
Keyword(s):  
Feature Extraction ◽  
Character Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Post Processing ◽  
Classification Technique ◽  
Scanned Image ◽  
Gujarati Language ◽  
High Degree ◽  
Selection Of

The entire character recognition system is logically characterized into different sections like Scanning, Pre-processing, Classification, Processing, and Post-processing. In the targeted system, the scanned image is first passed through pre-processing modules then feature extraction, classification in order to achieve a high recognition rate. This paper describes mainly on Feature extraction and Classification technique. These are the methodologies which play an important role to identify offline handwritten characters specifically in Gujarati language. Feature extraction provides methods with the help of which characters can identify uniquely and with high degree of accuracy. Feature extraction helps to find the shape contained in the pattern. Several techniques are available for feature extraction and classification, however the selection of an appropriate technique based on its input decides the degree of accuracy of recognition. 

OFFLINE YORÙBÁ HANDWRITTEN WORD RECOGNITION USING GEOMETRIC FEATURE EXTRACTION AND SUPPORT VECTOR MACHINE CLASSIFIER

2020 ◽  
Vol 5 (2) ◽  
pp. 504
Author(s):  
Matthias Omotayo Oladele ◽  
Temilola Morufat Adepoju ◽  
Olaide ` Abiodun Olatoke ◽  
Oluwaseun Adewale Ojo
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Word Recognition ◽  
Support Vector Machine Classifier ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Support Vector ◽  
Geometric Features ◽  
Total Length ◽  
Yoruba Language

Yorùbá language is one of the three main languages that is been spoken in Nigeria. It is a tonal language that carries an accent on the vowel alphabets. There are twenty-five (25) alphabets in Yorùbá language with one of the alphabets a digraph (GB). Due to the difficulty in typing handwritten Yorùbá documents, there is a need to develop a handwritten recognition system that can convert the handwritten texts to digital format. This study discusses the offline Yorùbá handwritten word recognition system (OYHWR) that recognizes Yorùbá uppercase alphabets. Handwritten characters and words were obtained from different writers using the paint application and M708 graphics tablets. The characters were used for training and the words were used for testing. Pre-processing was done on the images and the geometric features of the images were extracted using zoning and gradient-based feature extraction. Geometric features are the different line types that form a particular character such as the vertical, horizontal, and diagonal lines. The geometric features used are the number of horizontal lines, number of vertical lines, number of right diagonal lines, number of left diagonal lines, total length of all horizontal lines, total length of all vertical lines, total length of all right slanting lines, total length of all left-slanting lines and the area of the skeleton. The characters are divided into 9 zones and gradient feature extraction was used to extract the horizontal and vertical components and geometric features in each zone. The words were fed into the support vector machine classifier and the performance was evaluated based on recognition accuracy. Support vector machine is a two-class classifier, hence a multiclass SVM classifier least square support vector machine (LSSVM) was used for word recognition. The one vs one strategy and RBF kernel were used and the recognition accuracy obtained from the tested words ranges between 66.7%, 83.3%, 85.7%, 87.5%, and 100%. The low recognition rate for some of the words could be as a result of the similarity in the extracted features.

scholarly journals A Multiscale Chaotic Feature Extraction Method for Speaker Recognition

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Jiang Lin ◽  
Yi Yumei ◽  
Zhang Maosheng ◽  
Chen Defeng ◽  
Wang Chao ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Speaker Recognition ◽  
Extraction Method ◽  
State Of The Art ◽  
Recognition System ◽  
Nonlinear Dynamic Model ◽  
Feature Extraction Method ◽  
Analysis Technique ◽  
Recognition Systems ◽  
Environment Noise

In speaker recognition systems, feature extraction is a challenging task under environment noise conditions. To improve the robustness of the feature, we proposed a multiscale chaotic feature for speaker recognition. We use a multiresolution analysis technique to capture more finer information on different speakers in the frequency domain. Then, we extracted the speech chaotic characteristics based on the nonlinear dynamic model, which helps to improve the discrimination of features. Finally, we use a GMM-UBM model to develop a speaker recognition system. Our experimental results verified its good performance. Under clean speech and noise speech conditions, the ERR value of our method is reduced by 13.94% and 26.5% compared with the state-of-the-art method, respectively.

High Performance Unconstrained Word Recognition System Combining HMMs and Markov Random Fields

1997 ◽  
Vol 11 (05) ◽  
pp. 771-788 ◽  
Author(s):  
George Saon ◽  
Abdel Belaïd
Keyword(s):  
Word Recognition ◽  
Random Fields ◽  
Markov Random Fields ◽  
High Performance ◽  
Recognition Rate ◽  
Recognition System ◽  
State Observation ◽  
Markov Random ◽  
Word Images ◽  
Column Product

In this paper we present a system for the recognition of handwritten words on literal check amounts which advantageously combine HMMs and Markov random fields (MRFs). It operates at pixel level, in a holistic manner, on height normalized word images which are viewed as random field realizations. The HMM analyzes the image along the horizontal writing direction, in a specific state observation probability given by the column product of causal MRF-like pixel conditional probabilities. Aspects concerning definition, training and recognition via this type of model are developed throughout the paper. We report a 90.08% average word recognition rate on 2378 words and a 79.52% amount rate on 579 amounts of the SRTP* French postal check database (7031 words, 1779 amounts, different scriptors).

scholarly journals Face–Iris Multimodal Biometric Identification System

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 85 ◽  
Author(s):  
Basma Ammour ◽  
Larbi Boubchir ◽  
Toufik Bouden ◽  
Messaoud Ramdani
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Gabor Filter ◽  
Singular Spectrum Analysis ◽  
Recognition Rate ◽  
Evaluation Process ◽  
Relevant Information ◽  
Recognition System ◽  
Identification System ◽  
New Feature

Multimodal biometrics technology has recently gained interest due to its capacity to overcome certain inherent limitations of the single biometric modalities and to improve the overall recognition rate. A common biometric recognition system consists of sensing, feature extraction, and matching modules. The robustness of the system depends much more on the reliability to extract relevant information from the single biometric traits. This paper proposes a new feature extraction technique for a multimodal biometric system using face–iris traits. The iris feature extraction is carried out using an efficient multi-resolution 2D Log-Gabor filter to capture textural information in different scales and orientations. On the other hand, the facial features are computed using the powerful method of singular spectrum analysis (SSA) in conjunction with the wavelet transform. SSA aims at expanding signals or images into interpretable and physically meaningful components. In this study, SSA is applied and combined with the normal inverse Gaussian (NIG) statistical features derived from wavelet transform. The fusion process of relevant features from the two modalities are combined at a hybrid fusion level. The evaluation process is performed on a chimeric database and consists of Olivetti research laboratory (ORL) and face recognition technology (FERET) for face and Chinese academy of science institute of automation (CASIA) v3.0 iris image database (CASIA V3) interval for iris. Experimental results show the robustness.

A Structured Approach towards Robust Database Collection for Speaker Recognition

2017 ◽  
Vol 9 (3) ◽  
pp. 53 ◽  
Author(s):  
Pardeep Sangwan ◽  
Saurabh Bhardwaj
Keyword(s):  
Feature Extraction ◽  
Speaker Recognition ◽  
Recognition System ◽  
Classification Methods ◽  
Extraction Techniques ◽  
Recognition Phase ◽  
Speech Database ◽  
Biometric Systems ◽  
Recognition Systems ◽  
Structured Approach

<p>Speaker recognition systems are classified according to their database, feature extraction techniques and classification methods. It is analyzed that there is a much need to work upon all the dimensions of forensic speaker recognition systems from the very beginning phase of database collection to recognition phase. The present work provides a structured approach towards developing a robust speech database collection for efficient speaker recognition system. The database required for both systems is entirely different. The databases for biometric systems are readily available while databases for forensic speaker recognition system are scarce. The paper also presents several databases available for speaker recognition systems.</p><p> </p>

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