{"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/cnnlstm-architecture-for-speech-emotion","title":"CNN+LSTM Architecture for Speech Emotion Recognition with Data Augmentation","arxiv_id":"1802.05630","date":"2018-02-15","proceeding":null,"authors":["Caroline Etienne","Guillaume Fidanza","Andrei Petrovskii","Laurence Devillers","Benoit Schmauch"],"abstract":"In this work we design a neural network for recognizing emotions in speech,\nusing the IEMOCAP dataset. Following the latest advances in audio analysis, we\nuse an architecture involving both convolutional layers, for extracting\nhigh-level features from raw spectrograms, and recurrent ones for aggregating\nlong-term dependencies. We examine the techniques of data augmentation with\nvocal track length perturbation, layer-wise optimizer adjustment, batch\nnormalization of recurrent layers and obtain highly competitive results of\n64.5% for weighted accuracy and 61.7% for unweighted accuracy on four emotions.","url_abs":"http://arxiv.org/abs/1802.05630v2","url_pdf":"http://arxiv.org/pdf/1802.05630v2.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":[],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"emotion-recognition","task_name":"Emotion Recognition"},{"task_slug":"speech-emotion-recognition","task_name":"Speech Emotion Recognition"}],"methods":[{"method_slug":"convlstm","method_name":"ConvLSTM"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/speech-emotion-recognition-on-iemocap","task":"Speech Emotion Recognition","dataset":"IEMOCAP","model":"CNN+LSTM","rank_in_archive_order":7,"of":8,"metrics":{"UA":"0.650"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}