Comparison of conventional MFCC with new Efficient MFCC Extraction Method in Speech Recognition
نویسنده
چکیده
this paper introduces a new method of extracting MFCC for speech recognition and it is compared with the conventional MFCC method. The new algorithm reduces the calculation steps by 53% compared to conventional method. Simulation result indicates the new method has a recognition accuracy of 92.93% only 1.5% less than the conventional MFCC method which is has accuracy of 94.43%. However, the number of logic gates required to implement the new method is about half of the conventional MFCC method, which makes a new method very efficient in speech recognition.
منابع مشابه
Improving the performance of MFCC for Persian robust speech recognition
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...
متن کاملAutomatic Speaker Recognition using LPCC and MFCC
A person's voice contains various parameters that convey information such as emotion, gender, attitude, health and identity. This report talks about speaker recognition which deals with the subject of identifying a person based on their unique voiceprint present in their speech data. Pre-processing of the speech signal is performed before voice feature extraction. This process ensures the voice...
متن کاملتشخیص لهجه های زبان فارسی از روی سیگنال گفتار با استفاده از روش های استخراج ویژگی کارآمد و ترکیب طبقه بندها
Speech recognition has achieved great improvements recently. However, robustness is still one of the big problems, e.g. performance of recognition fluctuates sharply depending on the speaker, especially when the speaker has strong accent and difference Accents dramatically decrease the accuracy of an ASR system. In this paper we apply three new methods of feature extraction including Spectral C...
متن کاملRobust Speech Feature Extraction Using the Hilbert Transform Spectrum Estimation Method
The performance of traditional mel-frequency cepstral coefficients (MFCC) speech feature extraction method decreases drastically in the complex noisy environment. To improve the performance and robustness of speech recognition system, which is based on spectral envelope estimation method, the minimum distortionless response spectrum MVDR-MFCC (Minimum Variance Distortionless Response-MFCC) feat...
متن کاملEnvironment Independent Speech Recognition System using MFCC (Mel-frequency cepstral coefficient)
Speech recognition is a method of finding similarity between two sequences. Various researches have been done on it. In our research, we are trying to achieve the optimal accuracy during the recognition procedure. Here, we are extracting features of the voice sample before filtering it through a noise reduction filter. For each individual, there are number of features are taken using feature ex...
متن کامل