# Hierarchical Phoneme Classification for Improved Speech Recognition
## 5 Discussion  
In this paper, we proposed a hierarchical speech recognition model based on phoneme clustering. The proposed model first classifies the entire phoneme by a baseline phoneme classifier without considering phoneme characteristics. Then the retrained phoneme group models are used using the initial phoneme classification results. A confusion matrix of baseline phoneme classification was used to group each phoneme with similar characteristics. The confusion matrix shows which phonemes are not well distinguished. An analysis of phoneme characteristics in the TIMIT database revealed the differences of the phonemes in terms of lengths and autocorrelation values, so they were used in constructing phoneme group-specific models. We extracted features using different window size and window shift optimized for each phoneme group, and used autocorrelation as a feature as well as MFCC to improve the performance of the phoneme group models. The results of the experiment of the four consonant phoneme group models showed that the phoneme groups have different characteristics, and thus, the suitable models are different. The hierarchical phoneme classification model consisting of the baseline model and the phoneme group model was evaluated. The result shows that the phoneme specific feature extraction method and classification model have an improved performance of phoneme classification and speech recognition. One drawback of the proposed hierarchical classification method is that it does not improve the vowel classification performance because the proposed autocorrelation features and varying window sizes only help consonant classification. The MFCC is well suited to vowel classification and almost optimal classification performance is achieved, so very little improvement is expected.  
Conceptualization, [PERSON]-S.P., [PERSON]-H.K., and G.-J.J.; methodology, D.O.; software, D.O.; validation, D.O. and G.-J.J.; formal analysis, [PERSON]-H.K. and [PERSON]-S.P.; investigation, J.-H.K.; resources, G.-J.J. and [PERSON]-H.K.; data curation, D.O.; writing-original draft preparation, D.O. and G.-J.J.; writing-review and editing, [PERSON]-S.P. and [PERSON]-H.K.; visualization, D.O.; supervision, G.-J.J.; project administration, G.-J.J.; funding acquisition, G.-J.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Institute for Information and Communications Technology Promotion (IITP) grant funded by the Korean government (MSIT) (No. R7124-16-0004, Development of Intelligent Interaction Technology Based on Context Awareness and Human Intention Understanding), by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF-2017M3C1B6071400), and the BK21 Plus project funded by the Ministry of Education, Korea (21A20131600011).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{3}{c}{**Overall Model**} \\ \cline{3-4}
**Phoneme** & **Baseline** & **Group** & **MFCC** \\
**Groups** & **Phoneme** & **Specific** & **and** \\  & **Classification** & **Window Size** & **Combination Model** \\ \hline
**Vowels** & 0.700 & 0.700 & 0.700 \\
**Mixed** & 0.919 & 0.919 & 0.919 \\
**Nasals** & 0.568 & 0.579 & **0.590** \\
**Fracatives** & 0.724 & 0.739 & **0.754** \\
**Afficates** & 0.632 & **0.653** & **0.653** \\
**Stops** & 0.628 & 0.682 & **0.688** \\ \hline
**Group avg** & 0.695 & 0.712 & **0.717** \\
**Integrated** & 0.730 & 0.740 & **0.743** \\ \hline \hline \end{tabular}
\end{table}
Table 8: The results that baseline and integration phoneme group model.  
Data Availability Statement: The TIMIT dataset can be obtained from the Linguistic Data Consortium (LDC), [[https://www.ldc.upenn.edu](https://www.ldc.upenn.edu)]([https://www.ldc.upenn.edu](https://www.ldc.upenn.edu)).