# An Empirical Performance Analysis of the Speak Correct Computerized Interface
## 4 The Speak Correct Interactive Experiments  
Because of certain complexities in the Arabian accent, exercising the skill of speaking as a procedure of phonemes presents a significant challenge for speech recognition mechanisms, particularly for non-native English-speaking people. To address such issues, the proposed solution provokes users to say phrases and utterances, and then recognizes the phoneme sequence data of entry statements to classify submissions for them. As a result, training the Speak Correct disciplined component is a challenging task; each phrase with pronounced phonemes should be properly trained in order for the suggested scheme to recognize such phrases. \begin{table}
\begin{tabular}{c c} \hline \hline
**Modules** & **Description** \\ \hline The HMM model trainer & \begin{tabular}{c} Gather patterns in the data using the training data and save them mathematical analyses for the pronunciation verification process. \\ \end{tabular} \\ \hline Verification of HMM models & The platform’s acoustic HMM algorithms. \\ \hline Pronunciation hypotheses generator & \begin{tabular}{c} It examines a training activity and produces all \\ potential pronunciation alternatives, which are then \\ sent to the speech recognition system and tested \\ versus a spoken phrase. \\ \end{tabular} \\ \hline The HMM adapter & \begin{tabular}{c} It utilized to optimize system efficiency by adapting \\ acoustic models to every user’s acoustic attributes. \\ \end{tabular} \\ \hline The HMM decoder (ASR) & \begin{tabular}{c} The speech processor that recognizes the data from \\ the user. \\ \end{tabular} \\ \hline Confidence measure & \begin{tabular}{c} It takes the decoder’s n-best decrypted word \\ sequence and examines their ratings to decide \\ whether or not to transmit the outcome. \\ \end{tabular} \\ \hline The pronunciation errors analyzer & \begin{tabular}{c} Analyzes speech recognition outcomes and \\ generates feedback messages for the user. \\ \end{tabular} \\ \hline The intonation analyzer & \
\begin{tabular}{c} Evaluates the frequency curves of the user’s speech \\ and provides suggestions for prosodic and \\ rhythmic faults. \\ \end{tabular} \\ \hline Feedback generator &
\begin{tabular}{c} Map identifies mistakes to feed back comments that \\ describe the users’ shortcomings and provide \\ guidance on how to improve their pronunciation. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Different modules of the Speak Correct computerized interface. For analysis, the English term \"picture\" may be pronounced as \"/pik-cher/\" or \"/p k ch r/\"; if the user pronounced such a term as it is trained (unique frame order of the lattice chart), therefore, the output is correct. According to the literature [31; 32; 33], the speech recognition system is unreliable when the using phoneme recognition approach, and speech recognition achieves about 80% correctness. The authors developed a collaborative system that trains the word utterances and orders of the phonemes. With the help of the proposed Speak Correct computerized interface, users can correct mis-recognized phonemes by constructing improvement utterances according to the wave graph reactions. The proposed Speak Correct system aims to realize the correction of speaking English words; therefore, the originalities of the utterance evaluations are summarized as follows:  
* Word-based error correction. The Speak Correct interface's envisaged communication component allows the user to demonstrate improper phonemes based on the phoneme discrepancies of the pronounced statements. To successfully complete the speech correction, the platform must first identify the phoneme mistakes and then correct them after presenting the appropriate phoneme patterns. Throughout this engagement, the platform will recognize the users' statements using pre-defined grammatical structures, and then investigate and demonstrate the wave of such statements. * History-based evaluation.