منابع مشابه
Speech Recognition using MFCC and Neural Networks
The most common mode of communication between humans is speech. As this is the most preferred way, humans would like to use speech to interact with machines also. That is why, automatic speech recognition has gained a lot of popularity. Many approaches for speech recognition exist like Dynamic Time Warping (DTW), Hidden Markov Model (HMM). This paper shows how Neural Network (NN) can be used fo...
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The Mel Frequency cepstral coefficients are the most widely used feature in speech recognition but they are very sensitive to noise. In this paper to achieve a satisfactorily performance in Automatic Speech Recognition (ASR) applications we introduce a noise robust new set of MFCC vector estimated through following steps. First, spectral mean normalization is a pre-processing which applies to t...
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Abstract--The main objective of this research is to develop a speech emotion recognition system using residual phase and MFCC features with autoassociative neural network (AANN). The speech emotion recognition system classifies the speech emotion into predefined categories such as anger, fear, happy, neutral or sad. The proposed technique for speech emotion recognition (SER) has two phases : Fe...
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Speech is the most natural mode of communication. This work emphasizes on recognizing different emotions from speech signal. There are two major sections in this project namely feature extraction from speech signal and give this features as input to classifier to recognize emotions. Emotional states of speaker are considered as namely angry, happy, sad and neutral. The testing section classifie...
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This paper suggests Digital Signal processor (DSP) based speech recognition system with improved performance in terms of recognition accuracies and computational cost. The comprehensive surrey of various approaches of feature extraction like Mel filter banks with Mel Frequency Cepstrum Coefficients (MFCC). This paper describes an approach of isolated speech recognition by Digital Signal Process...
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ژورنال
عنوان ژورنال: International Journal of Advances in Applied Sciences
سال: 2015
ISSN: 2252-8814
DOI: 10.11591/ijaas.v4.i4.pp151-156