{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/evaluating-gammatone-frequency-cepstral","title":"Evaluating Gammatone Frequency Cepstral Coefficients with Neural Networks for Emotion Recognition from Speech","arxiv_id":"1806.09010","date":"2018-06-23","proceeding":null,"authors":["Gabrielle K. Liu"],"abstract":"Current approaches to speech emotion recognition focus on speech features\nthat can capture the emotional content of a speech signal. Mel Frequency\nCepstral Coefficients (MFCCs) are one of the most commonly used representations\nfor audio speech recognition and classification. This paper proposes Gammatone\nFrequency Cepstral Coefficients (GFCCs) as a potentially better representation\nof speech signals for emotion recognition. The effectiveness of MFCC and GFCC\nrepresentations are compared and evaluated over emotion and intensity\nclassification tasks with fully connected and recurrent neural network\narchitectures. The results provide evidence that GFCCs outperform MFCCs in\nspeech emotion recognition.","url_abs":"http://arxiv.org/abs/1806.09010v1","url_pdf":"http://arxiv.org/pdf/1806.09010v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"evaluating-gammatone-frequency-cepstral","repo_url":"https://github.com/SoyBison/gammatone","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"emotion-recognition","task_name":"Emotion Recognition"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"speech-emotion-recognition","task_name":"Speech Emotion Recognition"},{"task_slug":"speech-recognition","task_name":"Speech Recognition"},{"task_slug":"speech-recognition-1","task_name":"speech-recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}