{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T20:48:15Z","timestamp":1777495695644,"version":"3.51.4"},"reference-count":0,"publisher":"Innovative Publishers and Plagiarism Services","license":[{"start":{"date-parts":[[2024,4,21]],"date-time":"2024-04-21T00:00:00Z","timestamp":1713657600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-sa\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JSRT"],"abstract":"<jats:p>Speech emotion recognition (SER) is a burgeoning field in AI that analyzes vocal characteristics to understand human emotions. It delves deeper than the literal meaning of words, uncovering emotional cues hidden within speech patterns. Pitch, loudness, and speech rate are just a few features that vary with emotional state. SER utilizes machine learning algorithms to classify these features into categories like happiness, sadness, or anger. This technology offers a treasure trove of possibilities, from enhancing human-computer interaction to revolutionizing customer service and even aiding in mental health assessments. As SER continues to evolve, it holds the potential to transform how we connect with machines, fostering deeper understanding and richer emotional experiences.<\/jats:p>","DOI":"10.61808\/jsrt95","type":"journal-article","created":{"date-parts":[[2024,4,23]],"date-time":"2024-04-23T12:31:51Z","timestamp":1713875511000},"page":"30-34","source":"Crossref","is-referenced-by-count":1,"title":["Analysis Of Emotions Through Speech Recognition"],"prefix":"10.61808","author":[{"family":"Mr. Anandappa","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Mrs. Kavita Mudnal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"47981","published-online":{"date-parts":[[2024,4,21]]},"container-title":["Journal of Scientific Research and Technology"],"original-title":[],"link":[{"URL":"https:\/\/www.jsrtjournal.com\/index.php\/JSRT\/article\/download\/95\/120","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.jsrtjournal.com\/index.php\/JSRT\/article\/download\/95\/120","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,23]],"date-time":"2024-04-23T12:31:57Z","timestamp":1713875517000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jsrtjournal.com\/index.php\/JSRT\/article\/view\/95"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,21]]},"references-count":0,"URL":"https:\/\/doi.org\/10.61808\/jsrt95","relation":{},"ISSN":["2583-8660"],"issn-type":[{"value":"2583-8660","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,21]]}}}