A Speech Feature Extraction Algorithm Based on the Nonlinear Hopf Cochlea Model

2013 ◽  
Vol 416-417 ◽  
pp. 1176-1180
Author(s):  
Jian Guo Xing ◽  
Min Xu ◽  
Ji Xiang Zhu
Keyword(s):  
Feature Extraction ◽  
Speech Recognition ◽  
Filter Banks ◽  
Recognition Rate ◽  
Recognition System ◽  
Center Frequency ◽  
Characteristic Parameters ◽  
Discrete Cosine Transformation ◽  
Speech Feature ◽  
Speech Feature Extraction

The performance of speech recognition system is well or not is closely related to the characteristic parameters. For emulating human auditory system, a new method of speech feature extraction based on Hopf filter banks is presented. We modeled the extraction process of the MFCC, and used Hopf filter banks instead of the triangular filter banks. Then, we according the characteristics of Basilar Membranes in the cochlea to adjust the center frequency and bandwidth of the filter. The test speech goes through the Hopf filter banks, multi-dimensional eigenvectors will be obtained. After that, by Discrete Cosine Transformation, we will get the Hopf cepstral coefficients of the speech. Comparing with traditional feature MFCC, the speech recognition systems with Hopf characteristic parameters have better recognition rate and robustness characteristics in low Signal Noise Ratio (SNR) environment.

Features Extraction for Lhasa Tibetan Speech Recognition

2014 ◽  
Vol 571-572 ◽  
pp. 205-208
Author(s):  
Guan Yu Li ◽  
Hong Zhi Yu ◽  
Yong Hong Li ◽  
Ning Ma
Keyword(s):  
Speech Recognition ◽  
Linear Prediction ◽  
Recognition System ◽  
Continuous Speech Recognition ◽  
Mel Frequency Cepstral Coefficients ◽  
Linear Prediction Coefficient ◽  
Speech Feature ◽  
Perceptual Linear Prediction ◽  
Prediction Coefficient ◽  
Speech Feature Extraction

Speech feature extraction is discussed. Mel frequency cepstral coefficients (MFCC) and perceptual linear prediction coefficient (PLP) method is analyzed. These two types of features are extracted in Lhasa large vocabulary continuous speech recognition system. Then the recognition results are compared.

scholarly journals Iqro Reading Learning System through Speech Recognition Using Mel Frequency Cepstral Coefficient (MFCC) and Vector Quantization (VQ) Method

2018 ◽  
Vol 2 (01) ◽  
pp. 29
Author(s):  
Youllia Indrawaty Nurhasanah ◽  
Irma Amelia Dewi ◽  
Bagus Ade Saputro
Keyword(s):  
Feature Extraction ◽  
Speech Recognition ◽  
Vector Quantization ◽  
Recognition System ◽  
Learning System ◽  
Recognition Method ◽  
Signal Features ◽  
Extraction Step ◽  
Speech Feature ◽  
Mel Frequency Cepstral Coefficient

Historically, the study of Qur'an in Indonesia evolved along with the spread of Islam. Learning methods of reading the Qur'an have been found ranging from al-Baghdadi, al-Barqi, Qiraati, Iqro', Human, Tartila, and others, which can make it easier to learn to read the Qur'an. Currently, the development of speech recognition technology can be used for the detection of Iqro vol 3 reading pronunciations. Speech recognition consists of two general stages of feature extraction and speech matching. The feature extraction step is used to derive speech-feature and speech-matching stages to compare compatibility between test sound and train voice. The speech recognition method used to recognize Iqro readings is extracting speech signal features using Mel Frequency Cepstral Coefficient (MFCC) and classifying them using Vector Quantization (VQ) to get the appropriate speech results. The result of testing for speech recognition system of Iqro reading has been tested for 30 peoples as a sample of data and there are 6 utterances indicating the information failed, so the system has a success rate of 80%.

Dynamic Feature Extraction Method of Phone Speaker Based on Deep Learning

2020 ◽  
Vol 13 ◽  
Author(s):  
Hongbing Zhang
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Speech Recognition ◽  
Extraction Method ◽  
Good Effect ◽  
Dynamic Feature ◽  
Dynamic Features ◽  
Feature Extraction Method ◽  
Speech Feature ◽  
Speech Feature Extraction

: Nowadays, speech recognition has become one of the important technologies for human-computer interaction. Speech recognition is essentially a process of speech training and pattern recognition, which makes feature extraction technology particularly important. The quality of feature extraction is directly related to the accuracy of speech recognition. Dynamic feature parameters can effectively improve the accuracy of speech recognition, which makes the speech feature dynamic feature extraction has higher research value. The traditional dynamic feature extraction method is easy to generate more redundant information, resulting in low recognition accuracy. Therefore, based on a new speech feature extraction method, a method based on deep learning for speech feature extraction is proposed. Firstly, speech signal is preprocessed by pre-emphasis, windowing, filtering and endpoint detection. Then, the sliding differential cepstral feature (SDC) is extracted, which contains the voice information of the front and back frames. Finally, the feature is used as input to extract the dynamic features that represent the depth essence of speech information through the deep self-encoding neural network. The simulation results show that the dynamic features extracted by in-depth learning have better recognition performance than the original features, and have a good effect in speech recognition.

The Improved MFCC Speech Feature Extraction Method and its Application

2013 ◽  
Vol 756-759 ◽  
pp. 4059-4062 ◽  
Author(s):  
Xiao Yan Wang
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Recognition Rate ◽  
Feature Extraction Method ◽  
Low Snr ◽  
Nonlinear Properties ◽  
Speech Feature ◽  
Simulation Results ◽  
Robust To Noise ◽  
Speech Feature Extraction

Based on traditional MFCC feature, this paper suggests a new kind of speech signal feature: CMFCC by introducing the method of nonlinear properties. Simulation results indicate that the method has a strong robust to noise and is able to enhance the recognition rate under low SNR.

Research of Robust Feature for Speech Recognition

2012 ◽  
Vol 532-533 ◽  
pp. 1162-1166
Author(s):  
Xiang Hua Ren ◽  
Yun Xia Jiang
Keyword(s):  
Feature Extraction ◽  
Speech Recognition ◽  
Recognition Performance ◽  
Spectral Domain ◽  
Spectrum Estimation ◽  
Extraction Scheme ◽  
Speech Feature ◽  
Speech Recognizer ◽  
Derivatives Of ◽  
Speech Feature Extraction

Feature extraction plays an important role in speech recognition. In this paper, we propose a speech feature extraction scheme which focuses on increasing the robustness of speech recognizer in noise (additive) and channel (convolutive) distortion environment. Considering the two distortions are additive in spectral and log-spectral domain, respectively, we remove the additive components by computing the time derivatives of speech frames firstly in spectral domain and then in log-spectral domain. Compared with conventional methods, this method does not need spectrum estimation and prior knowledge of noise. Experimental results confirm that our proposed method can improve the speech recognition performance in environ-ments existing both noise and channel distortions.

Sign in / Sign up

Export Citation Format

Share Document