# A Novel Heterogeneous Parallel Convolution Bi-LSTM for Speech Emotion Recognition
## 6 Conclusions  
In this study, a novel heterogeneous parallel acoustic model called HPCB was proposed for speech emotion recognition. It exploits the spatiotemporal information more effectively. It is characterized by its two heterogeneous branch structures: the left one is composed of two dense layers and a Bi-LSTM layer, while the right one is composed of a dense layer, a convolution layer, and a Bi-LSTM layer. The 72D high-level statistical functions (HSF) features were calculated to verify the robustness and generalization of the model HPCB. Experimental results on the databases EMO-DB and CASIA suggest that HPCB demonstrate stably leading advantages over the previous methods in the literature.  
In the future, the effectiveness of HPCB will be further verified by applying it to other emotion databases and analyzing possible overfitting risks, HPCB can also be extended to other audio recognition or image classification problems for its superior learning capabilities. Furthermore, it will be compared with other deep learning models such as generative adversarial network (GAN) in SER besides zero-shot learning techniques.  
The proposed model demonstrates an impressive performance in SER by retrieving spatiotemporal information in deep learning. It suggests that the spatiotemporal signals extraction could be essential to achieve high-performance SER. On the other hand, how to decrease possible overfitting risk can be another interesting topic to explore further. This is because the proposed HPCB may face possible overfitting though the 0.5 dropout ratio is employed in learning for its complicated learning architecture. We plan to evaluate the learning performance in comparison with other peer deep learning models to query whether the integration of different types of neural networks will lead to an increase of overfitting and how should we overcome it efficiently if it were to happen.  
Conceptualization, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); methodology, H.Z.; software, Zhang. H.Y; validation, [PERSON] ([PERSON]) and H.Z.; formal analysis, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); investigation, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); resources, H.Z.; data curation, H.Z.; writing--original draft preparation, H.Z.; writing--review and editing, Zhang. H.Y, [PERSON]. ([PERSON]) and [PERSON]. ([PERSON]); visualization, [PERSON] ([PERSON]); supervision, [PERSON] ([PERSON]); project administration, [PERSON] ([PERSON]); funding acquisition, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by RESEARCH ON SPEECH RECOGNITION OF TIBETAN AMDO DIALECT BASED ON DEEP TRANSFER LEARNING, grant number 62066039. This research is supported by the Key Laboratory of Tibetan Information Processing, Ministry of Education,Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province (Grant: 2020-ZJ-Y05), Tibetan Information Engineering Technology Research Center of Qinghai Province.  
**Informed Consent Statement:** Not applicable.  
**Acknowledgments:** Thank you for your support from the computer school of Qinghai Normal University.  
**Conflicts of Interest:** The authors declare no conflict of interest.