systems Speech recognition provides text output to a given voice, in short, it is a speech to text (STT) conversion. It is useful for deaf, dumb and disabled people. This project aims to improve the efficiency of speech recognition accuracy. Developed the voice recognition system with its own dictionary, in order to improve the efficiency of the voice recognition system. Errors usually not only vary in numbers, but also have different degrees of impact on the optimization of a series of acoustic models. It is important to correct errors in speech recognition results to increase the performance of a speech recognition system. Errors are detected and corrected based on the database learned from correct-incorrect expression pairs. References and Assumptions values ​​and errors are displayed as the speech recognition system runs. By balancing errors we can improve the accuracy of speech recognition. By removing silence from the speech signal we can improve speech accuracy. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an Original Essay Speech recognition is a process of converting spoken words into text. Speech recognition analyzes an acoustic speech signal to identify the linguistic message. Speech recognition systems compare spoken words and text then provide accuracy. These recognition systems play a vital role in facilitating daily activities. Speech recognition applications include voice dialing, call routing and content-based spoken audio search, data entry, structured document preparation, speech-to-text and aircraft cockpit processing . Apart from these, the voice recognition system can be used for people with vision-related disabilities and paralyzed hands. In underdeveloped countries where the literacy rate is low, this can provide a mechanism for accessing information for people who are unable to read and write, as well as for people who may be literate but not skilled in computer skills. Speech recognition is defined as the ability of a computer to understand voice commands or responses which are an important factor in human-computer interaction. SR has been available for many years, but has not been practical due to the high cost of applications and computing resources. SR has had significant growth in telephony and voice-to-text applications. Increasing the efficiency of workers performing intensive typing, disability assistance and call center management while reducing staff costs shows the benefits of speech recognition. Speech recognition is the process by which a computer identifies spoken words. It basically means talking to your computer and making it correctly recognize what you are saying. It is simply a signal-to-symbol transformation, meaning it takes speech as input and provides text as output. Recognition models: Speaker-dependent: Speech recognition systems that can only recognize user speech that it is trained to understand are called speaker-dependent speech recognizers. Limited to include selected speakers. Speaker-independent: Speech recognition software that recognizes a variety of speakers, without any training, is called speaker-independent speech recognition. Hidden Markov Model: Every speech recognition system is associated with the Hidden Markov Model: a model ofHidden Markov is a probabilistic state machine that can be used to model and recognize speech. Consider the speech signal as a sequence of observable events generated by the mechanical speech production system that transitions from one state to another during speech production. The term hidden refers to the fact that the state of the system (i.e. the configuration of the speech articulators) is not known to the observer of the speech signal. Speech recognition systems use HMMs to model each sound unit in the language. In an HMM, each state is associated with a probability distribution that measures the probability of events generated by the state. These distributions are known as output or observation probability distributions. Each state is also associated with a set of transition probabilities. Given the current state, transition probabilities model the probability that the system will be in a certain state when the next observation is produced. Typically, Gaussian distributions are used to model the output distribution of each HMM state. Transition probabilities determine how quickly the model moves from one state to the next, giving the model some flexibility with respect to sound units that can vary in duration.HMM = (?, A, B)? = the probability vector of the initial state A = the transition matrix of the state B = the confusion matrix The definitions of HMM, there are three problems of interest: The evaluation problem: the forward-backward algorithm is used to find the probability that the model generated observations for a given model and sequence of observations. The decoding problem: The Viterbi algorithm can find the most probable sequence of states in the model that produced the observation for a given model and the sequence of observations. The learning problem: The Baum-Welch algorithm finds model parameters such that the maximum probability of generating observations for a given model and a sequence of observations. (A) Forward algorithm: The forward algorithm calculates all possible length state sequences that generate the observation sequence and then sums all probabilities. The probability of each path is the product of the state sequence probability and the joint probability along the path. (B) Viterbi algorithm: The forward algorithm calculates the probability that an HMM generates an observation sequence by summing the probabilities of all possible paths, so it does not provide the best path or state sequence. In many applications, it is desirable to find such a path. Finding the best one is the cornerstone of continuous speech recognition research. Since the state sequence is hidden in the HMM framework, the most used criterion is to find the state sequence that has the highest probability of being taken when generating the observation sequence. The Viterbi algorithm can be considered as dynamic programming applied to the HMM or as a modified forward algorithm. Instead of summing the probabilities of different paths arriving at the same destination state, the Viterbi algorithm chooses and remembers the best path. (C) Baum-Welch Algorithm: It is also known as forward-backward algorithm used to model observations in training data through HMM parameters. This algorithm is a kind of EM (Expectation Maximization) algorithm that iterates the data first in a forward pass and then in a backward pass. During each step, we adjust a set of probabilities to maximize the probability of a given observation in the training data corresponding to a given HMM state. Since this estimation problem has no analytical solution,.