Novel soft computing techniques based optimal noise reduction schemes for automatic speech recognition applications

Abstract

Automatic Speech Recognition (ASR) is a self-governing, computerbased newlinespoken language transcript for real time applications. Automatic speech newlinerecognition is a computer that hears with a microphone or phone, finds the newlinewords and helps a network to convert the written text. The purpose of an ASR newlinesystem is to correctly speak professionally speaking words and speaker for newlineself-reliant, environmental noise recorded by the device called microphone. newlineWhen ASR performance has seen overall improvements and relationships the newlinedegeneration continues in the presence of noise there must be a substantial newlinechallenge in the developing world applications of ASR. A common solution newlineto overcome the noise condition is the use of performance defect multi-level newlinetraining, where acoustic models are located train the data from the target newlinedomain. However, it is impossible to get realistic atmosphere level of training newlinestandards from all kinds of noise situations. Also, even multi-level training, newlineperformance compared to ASR systems are significantly worse clean newlinecontrolled test conditions. The purpose of this work meet the weakness issues newlinein noise cancellation of pre-processing, feature extraction and classifier newlinesections. Extensive work and research have been done in the field of speech newlinerecognition different languages. But the performance levels of speech newlinerecognition vary with factors language, databases, number of speakers, newlinedifferences among speakers etc. Once the ASR, which is created in a native newlinelanguage, allows you to communicate with a computer with a learning newlineenvironment. newline newline