Speech processing plays a crucial role in many signal processing applications, while the last decade has bought gigantic evolution based on machine learning prototype. Speech processing has a close relationship with computer linguistics, human–machine interaction, natural language processing, and psycholinguistics. This review article majorly discusses the feature extraction techniques and machine learning classifiers employed in speech processing and recognition activities. The performance of several machine learning techniques is validated for speech emotion recognition application on Berlin EmoDB database. Further, it gives the broad application areas and challenges in machine learning for speech processing. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.