In this paper, a novel approach is proposed for the recognition of Persian phonemes in the Persian Consonant-Vowel Combination (PCVC) speech dataset. Nowadays, deep neural networks play a crucial role in classification tasks. However, the best results in speech recognition are not yet as perfect as human recognition rate. Deep learning techniques show outstanding performance over many other classification tasks like image classification, document classification, etc. Furthermore, the performance is sometimes better than a human. The reason why automatic speech recognition (ASR) systems are not as qualified as the human speech recognition system, mostly depends on features of data which is fed to deep neural networks. Methods: In this research, firstly, the sound samples are cut for the exact extraction of phoneme sounds in 50ms samples. Then, phonemes are divided into 30 groups, containing 23 consonants, 6 vowels, and a silence phoneme. Results: The short-time Fourier transform (STFT) is conducted on them, and the results are given to PPNet (A new deep convolutional neural network architecture) classifier and a total average of 75.87% accuracy is reached which is the best result ever compared to other algorithms on separated Persian phonemes (Like in PCVC speech dataset). Conclusion: This method can be used not only for recognizing mono-phonemes but also it can be adopted as an input to the selection of the best words in speech transcription.

中文翻译：

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使用PPNet识别波斯语音素

在本文中，提出了一种在波斯语辅音-元音组合 (PCVC) 语音数据集中识别波斯语音素的新方法。如今，深度神经网络在分类任务中起着至关重要的作用。然而，语音识别的最佳结果还没有人类识别率那么完美。深度学习技术在许多其他分类任务（如图像分类、文档分类等）中表现出出色的性能。此外，性能有时比人类更好。自动语音识别（ASR）系统之所以不如人类语音识别系统合格，主要取决于输入深度神经网络的数据特征。方法：本研究首先对声音样本进行切割，以准确提取50ms样本中的音素声音。然后，音素分为 30 组，包含 23 个辅音、6 个元音和一个静音音素。结果：对它们进行短时傅里叶变换 (STFT)，并将结果提供给 PPNet（一种新的深度卷积神经网络架构）分类器，总平均达到 75.87% 的准确率，这是有史以来最好的结果到分离波斯语音素的其他算法（如在 PCVC 语音数据集中）。结论：该方法不仅可以用于识别单音素，还可以用作语音转录中最佳单词选择的输入。并将结果提供给 PPNet（一种新的深度卷积神经网络架构）分类器，总平均准确率达到 75.87%，这是与分离波斯语音素（如 PCVC 语音数据集）的其他算法相比有史以来最好的结果。结论：该方法不仅可以用于识别单音素，还可以用作语音转录中最佳单词选择的输入。并将结果提供给 PPNet（一种新的深度卷积神经网络架构）分类器，总平均准确率达到 75.87%，这是与分离波斯语音素（如 PCVC 语音数据集）的其他算法相比有史以来最好的结果。结论：该方法不仅可以用于识别单音素，还可以用作语音转录中最佳单词选择的输入。