چکیده (انگلیسی):

It is a well known fact that, speech recognition systems perform well when the system is used in conditions
similar to the one used to train the acoustic models. However, mismatches degrade the performance. In adverse
environment, it is very difficult to predict the category of noise in advance in case of real world environmental
noise and difficult to achieve environmental robustness. After doing rigorous experimental study it is observed
that, a unique method is not available that will clean the noisy speech as well as preserve the quality which have
been corrupted by real natural environmental (mixed) noise. It is also observed that only back-end techniques are
not sufficient to improve the performance of a speech recognition system. It is necessary to implement
performance improvement techniques at every step of back-end as well as front-end of the Automatic Speech
Recognition (ASR) model. Current recognition systems solve this problem using a technique called adaptation.
This study presents an experimental study that aims two points, first is to implement the hybrid method that will
take care of clarifying the speech signal as much as possible with all combinations of filters and enhancement
techniques. The second point is to develop a method for training all categories of noise that can adapt the acoustic
models for a new environment that will help to improve the performance of the speech recognizer under real
world environmental mismatched conditions. This experiment confirms that hybrid adaptation methods improve
the ASR performance on both levels, (Signal-to-Noise Ratio) SNR improvement as well as word recognition
accuracy in real world noisy environment.