Abstract
It is a well known fact that, speech recognition sy stems perform well when the system is used in condi tions similar to the one used to train the acoustic model s. However, mismatches degrade the performance. In adverse environment, it is very difficult to predic t the category of noise in advance in case of real world environmental noise and difficult to achieve enviro nmental robustness. After doing rigorous experiment al study it is observed that, a unique method is not a vailable that will clean the noisy speech as well a s preserve the quality which have been corrupted by r eal natural environmental (mixed) noise. It is also observed that only back-end techniques are not suff icient to improve the performance of a speech recognition system. It is necessary to implement pe rformance improvement techniques at every step of back-end as well as front-end of the Automatic Spee ch Recognition (ASR) model. Current recognition systems solve this problem using a technique called adaptation. This study presents an experimental st udy that aims two points, first is to implement the hyb rid method that will take care of clarifying the sp eech signal as much as possible with all combinations of filters and enhancement techniques. The second poi nt 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 performan ce 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.
Highlights
The performance of Automatic Speech Recognition (ASR) system based on acoustic model totally depends on the environment of training and testing data (Ding et al, 2010).In a speech recognition system, many parameters affect the accuracy of the system
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
Summary
The performance of Automatic Speech Recognition (ASR) system based on acoustic model totally depends on the environment of training and testing data (Ding et al, 2010). In a speech recognition system, many parameters affect the accuracy of the system. These parameters are speaker, isolated or continuous word recognition, size of vocabulary, language, environment conditions. Many of the approaches to build noise-robust recognition systems can be classified into one of the three primary categories: back-end adaptation techniques, front-end enhancement algorithms and alternative feature approaches. This class of technique focuses on adapting acoustic model parameters to better match the environmental conditions present
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