Name
Abstract | ||
|---|---|---|
Hidden Markov models (HMMs) have a long tradition in automatic speech recognition (ASR) due to their capability of capturing temporal dynamic characteristics of speech. For emotion recognition from speech, three HMM based architectures are investigated and compared throughout the current paper, namely, the Gaussian mixture model based HMMs (GMM-HMMs), the subspace based Gaussian mixture model based HMMs (SGMM-HMMs) and the hybrid deep neural network HMMs (DNN-HMMs). Extensive emotion recognition experiments are carried out on these three architectures on the CASIA corpus, the Emo-DB corpus and the IEMOCAP database, respectively, and results are compared with those of state-of-the-art approaches. These HMM based architectures prove capable of constituting an effective model for speech emotion recogntion. Also, the modeling accuracy is further enhanced by incorporating various advanced techniques from the ASR area. In particular, among all of the architectures, the SGMM-HMMs achieve the best performance in most of the experiments. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/icassp.2019.8683172 | 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) |
Keywords | Field | DocType |
Speech emotion recognition, hidden Markov models, subspace based GMM, hybrid DNN-HMM | Subspace topology,Pattern recognition,Emotion recognition,Computer science,Artificial intelligence,Artificial neural network,Hidden Markov model,Mixture model | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuiyang Mao | 1 | 1 | 1.37 |
| Dehua Tao | 2 | 0 | 1.01 |
| Guangyan Zhang | 3 | 0 | 2.37 |
| Pak-chung Ching | 4 | 1366 | 139.74 |
| Tan Lee | 5 | 476 | 74.69 |