# An Empirical Performance Analysis of the Speak Correct Computerized Interface
## 1 Introduction  
Speech recognition is the process of using computer software to turn speech into a series of words. Speech recognition's overall goal is to enable individuals to interact more easily and efficiently, because it is the most intuitive mode of interaction for individuals. Whereas long-term goals necessitate seamless integration with several natural language processing (NLP) components, there are a number of new applications that can be quickly deployed using the fundamental voice recognition module. Voice calling, call forwarding, record keeping and dictation, coordination and control, as well as computer-aided multilingual are just a few of the common applications. The technique of turning human sound impulses into words or commands is known as speech recognition. Automatic speech recognition (ASR), computer voice recognition, and speech to text (STT) are some of its other names. It includes computer engineering, linguistics, as well as technical knowledge and research [1; 2; 3; 4; 5; 6; 7; 8]. Speech correction is another form of speech recognition that is based on the sound of a person's voice. It is a subset of pattern classification and an attractive research areain voice speech accuracy enhancement. Speech correction research spans a wide range of disciplines, including computer science, machine intelligence, sensing applications, cognition, acoustics, linguistics, as well as information psychology. It is an interdisciplinary, all-encompassing research area. Distinct research topics have arisen as a result of the different research demands and restrictions. These domains can be separated into individual words, linked words, and persistent speech recognition algorithms, depending on the needs of the speaker's method of speaking. These domains can be separated into voice recognition algorithms for specific people and generic people based on the degree of reliance on the speaker. They can be classified into short vocabulary, moderate vocabulary, huge vocabulary, as well as infinite vocabulary voice recognition systems based on the size of their vocabulary [9; 10; 11; 12; 13; 14; 15]. People employ different recognition strategies and procedures for different voice recognition systems, but the essential concepts are the same. Feature extraction is applied to the gathered voice signals. The speech features acquired by the module are sent to the modeling library component. The speech pattern matching component finds speech segments based on the model library, and then calculates the recognition performance. Some speech recognition systems necessitate \"training\" (also known as \"enrollment\"), in which a single speaker delivers text or isolated vocabulary into the program. The technology analyses the person's unique voice and utilizes it to fine-tune speech recognition, resulting in a higher accuracy. \"Speaker-independent\" systems do not require training. \"Speaker dependent\" systems rely on training. The complexities of human communication have made growth difficult. It is one of the most difficult disciplines of computer science to master, as it combines linguistics, arithmetics, and analytics. The speech input, extraction and classification, vectorization, decoder, and word result are all elements of speech recognizers. To identify the proper output, the decoder uses acoustic models, a pronunciation dictionary, as well as language models. This paper discusses Speak Correct that is a computerized linguist interface. Speak Correct can assist its users in many different places around the world, when they have English pronunciation issues due to their Saudi or Egyptian accents. Further, in this study, the authors conduct an empirical evaluation of the Speak Correct computerized interface for an efficiency assessment.