A Gaussian Mixture Model Based Speech Recognition System Using Matlab
نویسندگان
چکیده
منابع مشابه
A Gaussian Mixture Model Based Speech Recognition System Using Matlab
This paper aims at development and performance analysis of a speaker dependent speech recognition system using MATLAB®. The issues that were considered are 1) Can Matlab, be effectively used to complete the aforementioned task, 2) Accuracy of the Gaussian Mixture Model used for parametric modelling, 3) Performance analysis of the system, 4) Performance of the Gaussian Mixture Model as a paramet...
متن کاملSpeaker Recognition System using Gaussian Mixture Model
In this paper,features for text-independent speaker recognition has been evaluated. Speaker identification from a set of templates and analyzing speaker recognition rate by extracting several key features like Mel Frequency Cepstral Coefficients [MFCC] from the speech signals of those persons by using the process of feature extraction using MATLAB2013 .These features are effectively captured us...
متن کاملSpeech Emotion Recognition by Gaussian Mixture Model
In the field of human computer interaction automatic speech emotion recognition is a current research topic. Emotion recognition in speech is a challenging problem because it is unclear that which features are effective for speech emotion recognition. In this paper we proposed an approach in which we extract the features of energy, spectral and acoustic domains and then merging these features b...
متن کاملUsing Gaussian mixture modeling in speech recognition
tive way to improve the performance of recognizers. This paper describe a speaker-independent isolated word recognition system which uses a well known technique, the combination of vector quantization with hidden Markov modeling. The conventional vector quantization algorithm is substituted by a statistical clustering algorithm, the ExpectationMaximization algorithm, in this system. Based on th...
متن کاملNoise spectrum estimation using Gaussian mixture model-based speech presence probability for robust speech recognition
This work presents a noise spectrum estimator based on the Gaussian mixture model (GMM)-based speech presence probability (SPP) for robust speech recognition. Estimated noise spectrum is then used to compute a subband a posteriori signal-to-noise ratio (SNR). A sigmoid shape weighting rule is formed based on this subband a posteriori SNR to enhance the speech spectrum in the auditory domain, wh...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Signal & Image Processing : An International Journal
سال: 2013
ISSN: 2229-3922,0976-710X
DOI: 10.5121/sipij.2013.4409