Speech emotion recognition using deep neural networks - Qadri, Syed Asif Ahmad (Kulliyyah of Engineering, International Islamic University Malaysia, 2020)
With the ever-increasing interest of research community in studying human- computer/human-human interactions, systems deducing and identifying emotional aspects of a speech signal has emerged as a hot research topic. Speech Emotion Recognition (SER) has brought the development of automated and intelligent analysis of human utterances to reality. Typically, a SER system focuses on extracting the features from speech signals such as pitch frequency, formant features, energy related and spectral features, tailing it with a classification quest to understand the underlying emotion. However, as of now there still exists a considerable amount of uncertainty arising from factors like, determining influencing features, development of hybrid algorithms, type and number of emotions and languages under consideration, etc. The key issues pivotal for successful SER system are driven by proper selection of proper emotional feature extraction techniques. In this research Mel- frequency Cepstral Coefficient (MFCC) and Teager Energy Operator (TEO) along with a new-fangled fusion of MFCC and TEO referred as Teager-MFCC (TMFCC) is examined over multilingual database consisting of English, German and Hindi languages. These datasets have been retrieved from authentic and widely adopted sources. The German corpus is the well-known Berlin Emo-DB, the Hindi corpus is Indian Institute of Technology Kharagpur Simulated Emotion Hindi Speech Corpus (IITKGP-SEHSC) and the English corpus is Toronto emotional speech set (TESS). Deep Neural Networks has been used for the classification of the different emotions considered viz., happy, sad, angry, and neutral. Evaluation results shows that MFCC with recognition rate of 87.8% outperforms TEO and TMFCC. With TEO and TMFCC configurations, the recognition rate has been found as 77.4% and 82.1% respectively. However, while considering energy-based emotions, contrasting results were fetched. TEO with recognition rate of 90.5% outperforms MFCC and TMFCC. With MFCC and TMFCC configurations, the recognition rate has been found as 83.7% and 86.7% respectively. The outcome of this research would assist information of a pragmatic emotional speech recognition implementation driven by wiser selection of underlying feature extraction techniques.