Sonia Sunny

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Speech recognition is a fascinating application of Digital Signal Processing and has many real-world applications. In this paper, a speech recognition system is developed for isolated spoken words using Discrete Wavelet Transforms (DWT) and Artificial Neural Networks (ANN). Speech signals are one-dimensional and are random in nature. Isolated words from(More)
Speech recognition is a fascinating application of digital signal processing offering unparalleled opportunities. In this paper, a comparative study of different feature extraction techniques like Linear Predictive Coding (LPC), Discrete Wavelet Transforms (DWT) and Wavelet packet Decomposition (WPD) are employed for recognizing speaker independent spoken(More)
Speech signals are one of the most important means of communication among the human beings. In this paper, a comparative study of two feature extraction techniques are carried out for recognizing speaker independent spoken isolated words. First one is a hybrid approach with Linear Predictive Coding (LPC) and Artificial Neural Networks (ANN) and the second(More)
Speech processing includes the various techniques such as speech coding, speech synthesis, speech recognition and speaker recognition. In the area of digital signal processing, speech processing has versatile applications so it is still an intensive field of research. Speech processing mostly performs two fundamental operations such as Feature Extraction(More)
 Abstract—Speech is a natural mode of communication for people and speech recognition is an intensive area of research due to its versatile applications. This paper presents a comparative study of various feature extraction methods based on wavelets for recognizing isolated spoken words. Neural Networks (ANN). The proposed method is implemented for 50(More)
Speech processing and consequent recognition are important areas of Digital Signal Processing since speech allows people to communicate more naturally and efficiently. In this work, a speech recognition system is developed for recognizing digits in Malayalam. For recognizing speech, features are to be extracted from speech and hence feature extraction(More)
— In this paper, a speech recognition system is developed for recognizing speaker-independent, isolated words. Speech recognition is a fascinating application of Digital Signal Processing and is a pattern classification task wherein an input pattern is classified as a sequence of stored patterns that have previously been learned. Isolated words in(More)
Signals are corrupted by additive noise and removing noise from speech signals is one of the major challenges of an automatic speech recognition problem. In this paper, a speech recognition system is developed for recognizing speaker independent isolated words in Malayalam. Voice signals are sampled directly from the microphone and the background noise are(More)
This work explores the use of a discrete wavelet transform, a feature extractor mechanism for speech recognition. Speech recognition is a fascinating application of digital signal processing offering unparalleled opportunities. The real-world applications deploying speech recognition and its implications can be varied across various fields. Speech(More)
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