Study On The Application Of Mathematical Morphology In Speech Recognition

There are kinds of noise in real circumstance, speech recognition rate is influenced seriously, so it seems very important to study noisy speech recognition. Form nonlinear theory of speech signal, this paper discusses the application of mathematical morphology for improving robustness of recognition. Speech enhancement, feature extraction and recognition method in noisy speech enhancement are studied. The main research work is as follows:1. Speech enhancement method based on morphological filter is studied. Noisy speech signals are enhanced using different morphological filters and structuring elements, output SNRs in different circumstances are acquired, and the influences of the shape and length of structure elements are analyzed.2. Morphological filter and wavelet transform are combined to form morphology-wavelet filter, speech signals with different noises are filtered. Experiments show that this filter can maintain signal shape and enhance signal, its effect is better than morphology filter.3. Based on idempotency of morphological filter,clean speech feature coefficients are extracted using morphology predistortion method. Feature distances of clean, noisy, denoisy and predistortion speech are analyzed and compared,and feasibility of predistortion method is achieved.4. On the basis of morphological filter, pitch detection methods are researched. According to the characters of short time average magnitude difference function (AMDF) and modified short time autocorrelation function(MACF), filtering weighted modified autocorrelation pitch detection method is designed. This method uses exponent of normalized AMDF to weight MACF, and realizes pitch detection of noisy speech.5. Predistortion feature coefficients are used in Hidden Markov Model(HMM) recognition method as training data in order to increase matching of training and recognizing process, and the speech recognition rates of this method are better than that of traditional method. 6. Speech recognition method of RBF neural networks based on predistortion coefficients is designed. The following research work is concerned with hidden centers choosing, weights computing and network structure optimizing. Influences of different criterions are analyzed, and an improving scheme is decided. Recognition rates of noisy speech using RBF neural networks and modified method based on predistortion coefficients are tested.