2 research outputs found
Arabic digits speech recognition and speaker identification in noisy environment using a hybrid model of VQ and GMM
This paper presents an automatic speaker identification and speech recognition for Arabic digits in noisy environment. In this work, the proposed system is able to identify the speaker after saving his voice in the database and adding noise. The mel frequency cepstral coefficients (MFCC) is the best approach used in building a program in the Matlab platform; also, the quantization is used for generating the codebooks. The Gaussian mixture modelling (GMM) algorithms are used to generate template, feature-matching purpose. In this paper, we have proposed a system based on MFCC-GMM and MFCC-VQ Approaches on the one hand and by using the Hybrid Approach MFCC-VQ-GMM on the other hand for speaker modeling. The White Gaussian noise is added to the clean speech at several signal-to-noise ratio (SNR) levels to test the system in a noisy environment. The proposed system gives good results in recognition rate
Speech Recognition in noisy environment using Deep Learning Neural Network
Recent researches in the field of automatic speaker recognition have shown that methods based
on deep learning neural networks provide better performance than other statistical classifiers. On
the other hand, these methods usually require adjustment of a significant number of parameters.
The goal of this thesis is to show that selecting appropriate value of parameters can significantly
improve speaker recognition performance of methods based on deep learning neural networks.
The reported study introduces an approach to automatic speaker recognition based on deep
neural networks and the stochastic gradient descent algorithm. It particularly focuses on three
parameters of the stochastic gradient descent algorithm: the learning rate, and the hidden and
input layer dropout rates. Additional attention was devoted to the research question of speaker
recognition under noisy conditions.
Thus, two experiments were conducted in the scope of this thesis. The first experiment was
intended to demonstrate that the optimization of the observed parameters of the stochastic
gradient descent algorithm can improve speaker recognition performance under no presence of
noise. This experiment was conducted in two phases. In the first phase, the recognition rate is
observed when the hidden layer dropout rate and the learning rate are varied, while the input
layer dropout rate was constant. In the second phase of this experiment, the recognition rate is
observed when the input layers dropout rate and learning rate are varied, while the hidden layer
dropout rate was constant. The second experiment was intended to show that the optimization of
the observed parameters of the stochastic gradient descent algorithm can improve speaker
recognition performance even under noisy conditions. Thus, different noise levels were
artificially applied on the original speech signal