With the development of big data computing, the speech recognition has been popular for serving human’s life. However, when place the speech recognition system into noisy environments, the background noises greatly degrades the speech recognition system accuracy as it adds in unuseful information into the desired speech. Thus for a speech recognition system, obtaining a good performance under noises has become a vital issue. To tackle the noise effect problem of automatic speech recognition (ASR), a method to reduce the noise effect is essential. Recently, multiple of methods have been developed to enhance the speech signal, they usually follow the principle of suppressing the noise in a noisy speech signal. This thesis simulated the popular techniques for speech recogniton and speech enhancement, which are the multilayer perceptron and the spectral subtraction. The aim of this work is to use MATLAB to build an automatic speech recognition system that can be used in noisy environment. MATLAB simulations are used to verify the success of recognition with clean speech and show the system performance improvements after applying speech enhancement method in seven kinds of noisy environments. The result is presented by using comparative histograms between noisy signals and corresponding denoised signals. It shows that, using denoised signal will obtain a higher recognition rate, thus we can say the system performance is improved in noisy environments.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:hig-20759 |
| Date | January 2015 |
| Creators | Li, Hongzhe |
| Publisher | Högskolan i Gävle, Avdelningen för elektronik, matematik och naturvetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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