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The main goal of automatic speech and speaker recognition (ASSR) is to transcribe natural speech and recognize its speaker. Recognizing a spoken sentence is obviously a knowledgeintensive process, which must take into account all variable information about the speech communication process. Out of several approaches, artificial intelligence approach has been observed to give remarkable results by visualizing, analysing and finally making a decision on the acoustic patterns. The present chapter deals with two basic processes for ASSR: modelling process and understanding process. The modelling process involves three stages: enhancement, segmentation and pre-processing. The understanding phase involves the recognition of the speech and the speaker through the known knowledge-based model. It has been observed from the literature that, in any speech processing system, because of channel coupling, many source signals mix together. Thus there may be a chance of variability in speech signals affecting the performance of the speech systems due to some factors like: background and channel noise, electrical noise from different sources, meaningless sounds (a sneeze) or filler words (‘uh’ or ‘um’) between words, different speaking rate, mood and styles of speakers. Due to this some signals may not be observed and may result to out-of-vocabulary (OOV) word problem during recognition or understanding process. Thus speech enhancement has played a vital role in the development of a perfect acoustic model or noise – free artificial word model (AWM) and vowel diphthong model (VDM). To process for speech enhancement in frequency – domain, the speech signal has to be split into frames using blind signal separation (BSS) method (Yilmaz and Richard 2004; Yamashita and Hirai 2004; Araki et al 2002; Murata et al 2001; G. J. Jang and Lee 2003; F. Bach and Jordan 2005; B. A. Pearl Mutter and Olsson 2006). Further the spectrum of the background noise has to be estimated by subtracting the noise spectrum from the spectrum of the frame (M. Berouli et al 1979; Yasser Ghanbari and Md. Reza 2004). It has been observed that some of the noise remains in the spectrum when the value of noise is greater than its mean. At the same time, some of the speech spectrum gets removed when noise is greater than the actual value of noise. In the spectrum this produces negative values and have to be set to zero. The overall effect puts a noise in the output signal known as

Modelling and Understanding of Speech and Speaker Recognition