The Advancements In Neural Networks And The On-demand Need For Accurate And Near Real-time Speech Emotion Recognition (SER) In Human–computer Interactions Make It Mandatory To Compare Available Methods And Databases In SER To Achieve Feasible Solutions And A Firmer Understanding Of This Open-ended Problem. The Proposed System Reviews Deep Learning Approaches For SER With Available Datasets, Followed By Conventional Machine Learning Techniques For Speech Emotion Recognition. Ultimately, We Present A Multi-aspect Comparison Between Practical Neural Network Approaches In Speech Emotion Recognition. The Goal Of This Study Is To Provide A Survey Of The Field Of Discrete Speech Emotion Recognition For The Customer Service By Analyzing The Customers Emotion Using Speech Recognition And Provide Rating According To The Emotions.
Speaker Recognition Is A Technique That Automatically Identifies A Speaker From A Recording Of Their Voice. Speaker Recognition Technologies Are Taking A New Trend Due To The Progress In Artificial Intelligence And Machine Learning And Have Been Widely Used In Many Domains. Continuing Research In The Field Of Speaker Recognition Has Now Spanned Over 50 Years. In Over Half A Century, A Great Deal Of Progress Has Been Made Towards Improving The Accuracy Of The System’s Decisions, Through The Use Of More Successful Machine Learning Algorithms. This Paper Presents The Development Of Automatic Speaker Recognition System Based On Optimised Machine Learning Algorithms. The Algorithms Are Optimised For Better And Improved Performance. Four Classifier Models, Namely, Support Vector Machines, ‘KNEAREST NEIGHBORS, RANDOM FOREST’, Logistic Regression, And Artificial Neural Networks Are Trained And Compared. The System Resulted With Artificial Neural Networks Obtaining The State-ofthe- Art Accuracy Of 96.03% Outperforming KNN, SVM, RF And LR Classifiers.
Music Genre Classification Utilizing CNN And RNN Algorithm Has Achieved Some Limited Success In Recent Years. Differences In Song Libraries, Machine Learning Techniques, Input Formats, And Types Of NNs Implemented Have All Had Varying Levels Of Success. This Article Reviews Some Of The Machine Learning Techniques Utilized In This Area. It Also Presents Research Work On Music Genre Classification. The Research Uses Images Of Spectrograms Generated From Timeslices Of Songs As The Input Into An NN To Classify The Songs Into Their Respective Musical Genres.
The Paper Describes Possibilities, Which Are Provided By Open APIs, And How To Use Them For Creating Unified Interfaces Using The Example Of Our Bot Based On Google API. In Last Decade AI Technologies Became Widespread And Easy To Implement And Use. One Of The Most Perspective Technology In The AI Field Is Speech Recognition As Part Of Natural Language Processing. New Speech Recognition Technologies And Methods Will Become A Central Part Of Future Life Because They Save A Lot Of Communication Time, Replacing Common Texting With Voice/audio. In Addition, This Paper Explores The Advantages And Disadvantages Of Well-known Chatbots. The Method Of Their Improvement Is Built. The Algorithms Of Rabin-Karp And Knut-Pratt Are Used. The Time Complexity Of Proposed Algorithm Is Compared With Existed One.
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