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تشخیص ارقام گفتاری فارسی با استفاده از شبکه های یادگیری عمیق | ||
| مدل سازی در مهندسی | ||
| دوره 21، شماره 74، آبان 1402، صفحه 163-172 اصل مقاله (575.34 K) | ||
| نوع مقاله: مقاله کامپیوتر | ||
| شناسه دیجیتال (DOI): 10.22075/jme.2023.30973.2472 | ||
| نویسندگان | ||
| سحر زربافی1؛ کورش کیانی* 2؛ راضیه راستگو3 | ||
| 1دانشگاه سمنان، دانشکده برق و کامپبوتر | ||
| 2دانشکده مهندسی برق و کامپیوتر دانشگاه سمنان | ||
| 3دانشکده برق و کامپیوتر دانشگاه سمنان | ||
| تاریخ دریافت: 27 خرداد 1402، تاریخ بازنگری: 01 مرداد 1402، تاریخ پذیرش: 30 مرداد 1402 | ||
| چکیده | ||
| طبقه‌بندی ارقام جدا شده چالش اساسی برای بسیاری از سیستم‌های طبقه‌بندی گفتار است. درحالی‌که کارهای زیادی بر روی زبان‌های گفتاری انجام شده است، تحقیقات محدودی در مورد داده‌های رقمی گفتاری فارسی در ادبیات گزارش شده است و تمامی تحقیقات مربوط به اعداد صفر تا 9 بوده است. برای این منظور، پایگاه داده ی جامعی شامل بازه ی وسیعتری از اعداد با مشارکت 145 نفر که شامل هفتاد نفر مرد و 75 نفر زن هستند، جمع‌آوری گردیده است. پایگاه داده مذکور، بازه عددی صفر تا 599 را پوشش می‌دهد. پس از پیش‌پردازش داده ها، داده‌های صوتی تبدیل به طیف‌نگار مل شده و برای استخراج ویژگی و طبقه‌بندی داده‌ها از شبکه عصبی کانولوشنی و نیز یک مدل ترکیبی شامل مدل ترنسفورمر و حافظه کوتاه و بلند مدت استفاده گردیده است. نتایج تجربی بر روی پایگاه داده جمع آوری شده حاکی از دقت اعتبارسنجی 98.03 درصد می باشد. آنالیزهای مختلفی نیز بر روی آزمایش و آزمون مدل ها صورت گرفته است. | ||
| کلیدواژه‌ها | ||
| ارقام گفتاری؛ طبقه بندی؛ ارقام گفتاری فارسی؛ طیف‌نگار مل؛ پایگاه داده؛ ترنسفورمر | ||
| عنوان مقاله [English] | ||
| Spoken Persian digits recognition using deep learning | ||
| نویسندگان [English] | ||
| Sahar Zarbafi1؛ Kourosh Kiani2؛ Razieh Rastgoo3 | ||
| 1M.Sc. Student, Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran | ||
| 2Associate Professor, Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran | ||
| 3Assistant Professor, Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran | ||
| چکیده [English] | ||
| Classification of isolated digits is a fundamental challenge for many speech classification systems. Previous works on spoken digits have been limited to the numbers 0 to 9. In this paper, we propose two deep learning-based models for spoken digit recognition in the range of 0 to 599. The first model is a Convolutional Neural Network (CNN) model that uses the Mel spectrogram obtained from the audio data. The second model uses the recent advances in deep sequential models, especially the Transformer model followed by a Long Short-Term Memory (LSTM) Network and a classifier. Moreover, we also collected a dataset, including audio data by a contribution of 145 people, covering the numerical range from 0 to 599. The experimental results on the collected dataset indicate a validation accuracy of 98.03%. | ||
| کلیدواژه‌ها [English] | ||
| Spoken digits, Persian digits, Deep learning, Convolutional Neural Network (CNN), Mel spectrogram, Transformer | ||
| مراجع | ||
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