{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:00:58Z","timestamp":1777705258959,"version":"3.51.4"},"reference-count":14,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2024,4,18]]},"abstract":"<jats:p>Non-speech emotion recognition involves identifying emotions conveyed through non-verbal vocalizations such as laughter, crying, and other sound signals, which play a crucial role in emotional expression and transmission. This paper employs a nine-category discrete emotion model encompassing happy, sad, angry, peaceful, fearful, loving, hateful, brave, and neutral. A proprietary non-speech dataset comprising 2337 instances was utilized, with 384-dimensional feature vectors extracted. The traditional Backpropagation Neural Network (BPNN) algorithm achieved a recognition rate of 87.7% on the non-speech dataset. In contrast, the proposed Whale Optimization Algorithm - Backpropagation Neural Network (WOA-BPNN) algorithm, applied to a self-made non-speech dataset, demonstrated a remarkable accuracy of 98.6%. Notably, even without facial emotional cues, non-speech sounds effectively convey dynamic information, and the proposed algorithm excels in their recognition. The study underscores the importance of non-speech emotional signals in communication, especially with the continuous advancement of artificial intelligence technology. The abstract thus encapsulates the paper\u2019s focus on leveraging AI algorithms for high-precision non-speech emotion recognition.<\/jats:p>","DOI":"10.3233\/jifs-238700","type":"journal-article","created":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T12:48:51Z","timestamp":1710247731000},"page":"11067-11077","source":"Crossref","is-referenced-by-count":0,"title":["Non-speech emotion recognition based on back propagation feed forward networks"],"prefix":"10.1177","volume":"46","author":[{"given":"Xiwen","family":"Zhang","sequence":"first","affiliation":[{"name":"Control Science and Engineering, Tongji University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Xiao","sequence":"additional","affiliation":[{"name":"Control Science and Engineering, Tongji University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-238700_ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TSA.2005.858055"},{"key":"10.3233\/JIFS-238700_ref2","doi-asserted-by":"publisher","DOI":"10.3390\/s23031355"},{"issue":"4","key":"10.3233\/JIFS-238700_ref5","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1007\/s10772-021-09883-3","article-title":"Speech emotion recognition using data augmentation","volume":"25","author":"Praseetha","year":"2022","journal-title":"International Journal of Speech Technology"},{"key":"10.3233\/JIFS-238700_ref6","doi-asserted-by":"crossref","first-page":"156","DOI":"10.21437\/Interspeech.2022-10943","article-title":"Mind the gap: On the value of silence representations to lexical-based speech emotion recognition","volume":"2022","author":"Perez","year":"2022","journal-title":"Proc. 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