Speechat; an algorithm for real-time Spoken Language Assessment in python

This algorithm <a href="https://shahabks.github.io/Speechat/" rel="noopener ugc nofollow" target="_blank">https://shahabks.github.io/Speechat/</a> was built for processing high-entropy speech (simultaneous free speech processing) using probabilistic machine learning and deep learning models to predict spoken English language proficiency. This algorithm can measure the “pronunciation”, “prosody”, “use of language” competency and latent semantic index of a user (speaker) to rate its spoken proficiency based on a classification scores and also compare it with the average rate of non-native and native speakers. This is the results from two years of study whose overall achievement is an average assessment accuracy level of 72% for non-native adult speakers. The correlation between the human scores and the machine scores for an overall measure of speaking was 0.86 thus proving the reliability of the measure of speaking in tests. <a href="https://sabailabo.medium.com/auto-speech-rater-an-algorithm-for-real-time-spoken-english-assessment-in-python-52e6432e6941">Read More</a>