A Connectionist Framework For Feature Based Speech Recognition System Using Artificial Neural Networks Nalini Vasudevan * Anushruthi Rai † Arjun Jain ‡ Abstract Keywords: formants, fourier transform, dft, dwt, lpc, template matching, dynamic warp- ing, artificial neural networks In this paper we study the various methods employed to recognize discrete speech.We de- sign a recognition system which is capable of recognizing spoken language. The soft- ware takes spoken language and translates it into written text, or follow the spoken in- structions to perform other functions. Here, we propose an unexampled method to recog- nize speech.We provide a basic connectionist framework to analyze speech wave.The spo- ken words are digitized(turned into sequence of numbers) and matched against pretrained samples in order to identify the words. The system is trained, requiring samples of ac- tual words that will be spoken by the user of the system. The sample words are digitized, stored in the computer to match against fu- ture words. We propose a novel combination of extracting the characterestics of the au- dio signal using linear predictive coding and * R.V. College of Engineering, Computer Science & Engineering, Bangalore, India 560059 Tel: +91 94481 07482 Email: naliniv@gmail.com † R.V. College of Engineering, Computer Science & Engineering, Bangalore, India 560059 Tel: +91 98451 07687 Email: anu shruthi@yahoo.com ‡ R.V. College of Engineering, Computer Science & Engineering, Bangalore, India 560059 Tel: +91 99451 24241 Email: arjunjain@gmail.com a computational approach of using artificial neural networks in indentifying the correct sample. The analog audio is converted into digital signals. This requires analog-to-digital conversion. Linear Predictive Coding is a cor- relation measure, a measure of similarity be- tween two signals, and is used in the analysis of speech in our implementation. As speech recognition involves the ability to match a voice pattern against a provided or acquired vocabulary, a neural net is constructed to achieve maximum accuracy We show that this method gives salutary results using experi- mental observations. Then we provide condi- tions under which the system gives optimum results. 1 Introduction Speech Recognition is the field of computer science that deals with designing computer systems that can recognize spoken words. They generally require an extended training session during which the computer system be- comes accustomed to a particular voice and accent. Such systems are said to be speaker dependent. Many systems also require that the speaker speak slowly and distinctly and separate each word with a short pause. These systems are called discrete speech systems. Recently, great strides have been made in con- tinuous speech systems – voice recognition systems that allow you to speak naturally. There are now several continuous-speech sys-