Speech is the most natural way of communication among humans. This mode of communication is constituted of two parts, namely sound and sense. The intelligent production and synthesis of speech has intrigued man himself for long and efforts at automated speech recognition, has gone through various phases. Hidden Markov Models (HMMs) provide a simple and effective framework for modeling time-varying spectral vector sequences. Application of HMMs to speech recognition has seen considerable success and gained much popularity. As a consequence, almost all present day speech recognition systems are based on HMMs.The current paper presents a brief study on the HMM based technique applied to speech recognition and also discusses the issues and limitations of HMMs in speech processing.

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Image compression is a highly essential part of image processing and is a necessity of the modern world required in various fields. It is a process of representing image data using fewer bits than it is required for the original, by performing image compression a certain amount of data used by the image for its storage can be reduced. Compression is necessary in cases where a large amount of data is to be stored or transferred.This paper reviews some of the conventional methods for achieving Image compression, viz. Run length encoding, DCT, DWT to name a few. Artificial neural networks can also be used to achieve image compression. Here, an attempt is made to compare between the traditional methods of performing image compression and the artificial neural network approach.