"Red is apple the." Constraints are often represented by grammar. Read vs. Spontaneous Speech – When a person reads it's usually in a context that has been previously prepared, but when a person uses spontaneous speech, it is difficult to recognize the speech because of the disfluencies (like "uh" and "um", false starts, incomplete sentences, stuttering, coughing, and laughter) and limited vocabulary. Adverse conditions – Environmental noise (e.g. Noise in a car or a factory). Acoustical distortions (e.g. echoes, room acoustics) Speech recognition is a multi-leveled pattern recognition task. Acoustical signals are structured into a hierarchy of units, e.g. Phonemes, Words, Phrases, and Sentences; Each level provides additional constraints; e.g. Known word pronunciations or legal word sequences, which can compensate for errors or uncertainties at a lower level; This hierarchy of constraints is exploited. By combining decisions probabilistically at all lower levels, and making more deterministic decisions only at the highest level, speech recognition by a machine is a process broken into several phases. Computationally, it is a problem in which a sound pattern has to be recognized or classified into a category that represents a meaning to a human. Every acoustic signal can be broken into smaller more basic sub-signals. As the more complex sound signal is broken into the smaller sub-sounds, different levels are created, where at the top level we have complex sounds, which are made of simpler sounds on the lower level, and going to lower levels, even more, we create more basic and shorter and simpler sounds. At the lowest level, where the sounds are the most fundamental, a machine would check for simple and more probabilistic rules of what sound should represent. Once these sounds are put together into more complex sounds on upper level, a new set of more deterministic rules should predict what the new complex sound should represent. The most upper level of a deterministic rule should figure out the meaning of complex expressions. In order to expand our knowledge about speech recognition, we need to take into consideration neural networks. There are four steps of neural network approaches: Digitize the speech that we want to recognize For telephone speech the sampling rate is 8000 samples per second; Compute features of spectral-domain of the speech (with Fourier transform); computed every 10 ms, with one 10 ms section called a frame; Analysis of four-step neural network approaches can be explained by further