Isolated Word Recognition Using Ergodic Hidden Markov Models and Genetic Algorithm

Speech to text was one of speech recognition applications which speech signal was processed, recognized and converted into a textual representation. Hidden Markov model (HMM) was the widely used method in speech recognition. However, the level of accuracy using HMM was strongly influenced by the optimalization of extraction process and modellling methods. Hence in this research, the use of genetic algorithm (GA) method to optimize the Ergodic HMM was tested. In Hybrid HMM-GA, GA was used to optimize the Baum-Welch method in the training process. It was useful to improve the accuracy of the recognition result which is produced by the HMM parameters that generate the low accuracy when the HMM are tested. Based on the research, the percentage increases the level of accuracy of 20% to 41%. Proved that the combination of GA in HMM method can gives more optimal results when compared with the HMM system that not combine with any method

Genetic Algorithm for Combined Speaker and Speech Recognition using Deep Neural Networks, Journal of Telecommunications and Information Technology, 2018, nr 2

Huge growth is observed in the speech and speaker recognition field due to many artificial intelligence algorithms being applied. Speech is used to convey messages via the language being spoken, emotions, gender and speaker identity. Many real applications in healthcare are based upon speech and speaker recognition, e.g. a voice-controlled wheelchair helps control the chair. In this paper, we use a genetic algorithm (GA) for combined speaker and speech recognition, relying on optimized Mel Frequency Cepstral Coefficient (MFCC) speech features, and classification is performed using a Deep Neural Network (DNN). In the first phase, feature extraction using MFCC is executed. Then, feature optimization is performed using GA. In the second phase training is conducted using DNN. Evaluation and validation of the proposed work model is done by setting a real environment, and efficiency is calculated on the basis of such parameters as accuracy, precision rate, recall rate, sensitivity, and specificity. Also, this paper presents an evaluation of such feature extraction methods as linear predictive coding coefficient (LPCC), perceptual linear prediction (PLP), mel frequency cepstral coefficients (MFCC) and relative spectra filtering (RASTA), with all of them used for combined speaker and speech recognition systems. A comparison of different methods based on existing techniques for both clean and noisy environments is made as well

Using Genetic Algorithm to Improve the Performance of Speech Recognition Based on Artificial Neural Network

The development for speech recognition system has been for a while. The recognition platform can be divided into three types. Dynamic Time Warping (DTW) (Sakoe, 1978), th

Application of neural networks in whispered speech recognition.

Nedavno postignuti uspesi dubinskih neuralnih mreža u različitim zadacima mašinskog učenja su doprineli da vestačke neuralne mreze ponovo zauzmu bitnu ulogu u automatskom prepoznavanju govora. U ovom doktoratu je ispitana primena vestačkih neuralnih mreza u prepoznavanju šapata...The recent success of Deep Neural Networks (DNN) in different machine learning tasks has significantly contributed to the rise in the popularity of artificial neural networks (ANN) and their today’s role in Automatic Speech Recognition (ASR). This thesis examines how artificial neural networks can benefit in automatic whispered speech recognition..

Journal of Telecommunications and Information Technology, 2018, nr 2

kwartalni