09-03-2011, 11:20 AM
Presented by:
G.Nagendra babu
[attachment=9844]
SPEECH RECOGNITION USING DSP
All speaker recognition systems contain two main modules feature extraction and feature matching.
1.Feature extraction is the process that extracts a small amount of data from the voice signal that can later be used to represent each speaker.
2.Feature matching involves the procedure to identify the unknown speaker by comparing extracted features from his/her voice input with the ones from a set of known speakers.
Fast Fourier Transform (FFT) :
The next processing step is the Fast Fourier Transform, which converts each frame of N samples from the time domain into the frequency domain. The FFT is a fast algorithm to implement the Discrete Fourier Transform (DFT) which is defined on the set of N samples {xn},
Conclusion:
Even though much care is taken it is difficult to obtain an efficient speaker recognition system since this task has been challenged by the highly variant input speech signals.
Speech signals in training and testing sessions can be greatly different due to many facts such as people voice change with time, health conditions (e.g. the speaker has a cold), speaking rates, etc.
There are also other factors, beyond speaker variability, that present a challenge to speaker recognition technology. Because of all these difficulties this technology is still an active area of research
G.Nagendra babu
[attachment=9844]
SPEECH RECOGNITION USING DSP
All speaker recognition systems contain two main modules feature extraction and feature matching.
1.Feature extraction is the process that extracts a small amount of data from the voice signal that can later be used to represent each speaker.
2.Feature matching involves the procedure to identify the unknown speaker by comparing extracted features from his/her voice input with the ones from a set of known speakers.
Fast Fourier Transform (FFT) :
The next processing step is the Fast Fourier Transform, which converts each frame of N samples from the time domain into the frequency domain. The FFT is a fast algorithm to implement the Discrete Fourier Transform (DFT) which is defined on the set of N samples {xn},
Conclusion:
Even though much care is taken it is difficult to obtain an efficient speaker recognition system since this task has been challenged by the highly variant input speech signals.
Speech signals in training and testing sessions can be greatly different due to many facts such as people voice change with time, health conditions (e.g. the speaker has a cold), speaking rates, etc.
There are also other factors, beyond speaker variability, that present a challenge to speaker recognition technology. Because of all these difficulties this technology is still an active area of research
