# An Acoustic Feature-Based Deep Learning Model for Automatic Thai Vowel Pronunciation Recognition
## 3 Related Works
### Speech Recognition Tasks  
As deep learning architectures, CNNs have been applied in speech recognition. The CNN model was applied to ASR [37] with various strategies such as pooling and weight sharing, which improved the experimental results. The CNN architecture was applied to classify 14 phrases in a small-footprint keyword spotting task [37] and achieved 27% to 44% improvement in the false rejection rate. The best CNN architecture and strategies were employed in large-scale speech tasks [38] and in noise-robust speech recognition [39]. The three large vocabularies continuous speech recognition (LVCSR) tasks were a 50 h and a 400 h news broadcast and a 300 h switchboard. The large-scale speech tasks obtained a word error rate (WER) of 12% to 14% relative improvement. In Aurora4, noise-robust speech recognition obtained 8.81% WER and 10.0% relative reduction over the traditional CNN on the augmented multiparty interaction meeting transcription task. Two techniques that are used to improve the performance and increase the robustness of CNN acoustic models [40] were autoregressive moving average (ARMA) spectrogram features and channel dropout. The channel dropout technique obtained 16% WER with ARMA features and 20% WER with FBANK features over the baseline CNN. The Urdu numerals 0-9 were classified into 10 classes [41]; Urdu is the national language of Pakistan and spoke in parts of India. The public dataset consisted of 25,518 sound samples that were collected from 740 speakers. A CNN for audio digit classification with Mel spectrogram received 97.53%. A phonetic posteriorgram (PPG) speech feature with CNN was applied in speech command control-based recognition [42]. The dataset was created by 3 cerebral palsy (CP) patients who spoke 19 Mandarin commands 10 times each. The data augmentation method was applied to receive 103 (100 corruption with noise data and 3 time-domain variances). The 19 Mandarin commands were close, up, down, previous, next, in, out, left, right, home, one, two, three, four, five, six, seven, eight, and nine. These commands were developed for controlling web browser applications using speech. The results presented that the CNN with PPG achieved 93.49% accuracy. The CNN-PPG system was better than the CNN with MFCC and ASR-based systems, which obtained 65.67% and 89.59%, respectively. For vowel speech recognition, the Thai vowel speech recognition task [43] used the convolutional neural network of the Thai Simple Vowel model with MFCCs. The noisy Thai vowel dataset used in the task was collected from 50 informants. The dataset consisted of 1800 vowel sound files. The output consisted of 18 classes. The results obtained 90.00% and 88.89% accuracy in the female and male datasets, respectively. The CNN was also applied to the vowels in Javanese, one of the languages spoken in Indonesia [44; 45]. Mel frequency spectral coefficients (MFSCs) were applied to separate the features. The dataset consisted of 250 middle vowel sound files recorded by a Javanese speaker, and the output consisted of 5 classes. The results achieved 94% and 99.6% accuracy, respectively.