USING SOMS AS FEATURE EXTRACTORS FOR SPEECH RECOGNITIONJari
نویسندگان
چکیده
In this paper we demonstrate that the Self-Organizing Maps of Kohonen can be used as speech feature ex-tractors that are able to take temporal context into account. We have investigated two alternatives to use SOMs as such feature extractors, one based on tracing the location of highest activity on a SOM, the other on integrating the activity of the whole SOM for a period of time. The experiments indicated that an improvement is achievable by using these methods.
منابع مشابه
Using phonetic feature extraction to determine optimal speech regions for maximising the effectiveness of glottal source analysis
Parameterisation of the glottal source has become increasingly useful for speech technology. For many applications it may be desirable to restrict the glottal source feature data to only speech regions where it can be reliably extracted. In this paper we exploit the previously proposed set of binary phonetic feature extractors to help determine optimal regions for glottal source analysis. Besid...
متن کاملRegularized MVDR spectrum estimation-based robust feature extractors for speech recognition
In this paper, we present two robust feature extractors that use a regularized minimum variance distortionless response (RMVDR) spectrum estimator instead of the discrete Fourier transform-based direct spectrum estimator, used in many front-ends including the conventional MFCC, for estimating the speech power spectrum. Direct spectrum estimators, e.g., single tapered periodogram, have high vari...
متن کاملNebula: F0 Estimation and Voicing Detection by Modeling the Statistical Properties of Feature Extractors
A F0 and voicing status estimation algorithm for speech analysis/synthesis is proposed. Instead of directly modeling speech signals, the proposed algorithm models the behavior of feature extractors under additive noise using a bank of Gaussian mixture models, trained on artificial data generated from Monte-Carlo simulations. The conditional distributions of F0 predicted by the GMMs are combined...
متن کاملImproving of Feature Selection in Speech Emotion Recognition Based-on Hybrid Evolutionary Algorithms
One of the important issues in speech emotion recognizing is selecting of appropriate feature sets in order to improve the detection rate and classification accuracy. In last studies researchers tried to select the appropriate features for classification by using the selecting and reducing the space of features methods, such as the Fisher and PCA. In this research, a hybrid evolutionary algorit...
متن کاملA Database for Automatic Persian Speech Emotion Recognition: Collection, Processing and Evaluation
Abstract Recent developments in robotics automation have motivated researchers to improve the efficiency of interactive systems by making a natural man-machine interaction. Since speech is the most popular method of communication, recognizing human emotions from speech signal becomes a challenging research topic known as Speech Emotion Recognition (SER). In this study, we propose a Persian em...
متن کامل