As　the　importance　of　human-computer　interaction　(HCI)　continues　to　strengthen　and　the　field　of　deep　learning　evolves,　numerous　models　have　found　their　application　in　the　realm　of　Speech　Emotion　Recognition　(SER),　leading　to　significant　advancements　in　recent　years.　However,　effectively　recognizing　and　processing　human　emotions　through　computational　systems　remains　a　complex　and　formidable　challenge.　This　review　aims　to　provide　a　comprehensive　summary　of　the　latest　accomplishments　in　SER,　encompassing　a　diverse　range　of　application　scenarios,　from　education　and　healthcare　to　criminal　investigation.　Additionally,　it　delves　into　various　models　and　preprocessing　techniques　such　as　Convolutional　Neural　Networks　(CNN),　Convolutional　Recurrent　Neural　Networks　(CRNN),　Long　Short-Term　Memory　(LSTM),　and　datasets　like　RAVDESS　and　RECOLA,　which　encompass　a　wide　array　of　scenes　and　languages.　While　the　recent　strides　in　SER　have　undeniably　achieved　impressive　accuracy　rates,　a　notable　gap　exists　in　research　that　addresses　more　intricate　emotional　contexts,　including　situations　involving　irony　or　sarcasm.　Consequently,　this　review　focuses　on　a　comprehensive　analysis　of　the　limitations　inherent　in　different　feature　engineering　strategies.　Moreover,　it　investigates　the　challenge　of　interpretability　posed　by　complex　models,　the　constraint　posed　by　singular　and　hard-to-gather　datasets,　and　the　expansive　scope　of　potential　applications　SER　could　serve.　Considering　these　complexities,　a　potential　pathway　to　further　enhance　SER＇s　effectiveness　and　applicability　is　proposed.　This　involves　exploring　the　concept　of　non-binary　emotion　classification,　harnessing　rich　contextual　information,　and　integrating　datasets　that　incorporate　gesture　and　textual　data.　By　adapting　feature　extraction　techniques　to　align　with　the　unique　demands　of　specific　scenarios,　the　performance　of　SER　models　could　be　markedly　improved.　©　2024　SPIE.