ترجمه فارسی مقاله پاکت طیفی چهارگانه و طبقه‌بندی 1 بعدی-سی‌ان‌ان از گفتار به طور معمول صدادار و زمزمه‌شده

• کتاب صدها نکته فارسی (خودمونی) – نسخه PDF — زبان ساده و کاربردی
  مشاهده نمونه نسخه نکات ساده
• کتاب صدها نکته رسمی فارسی – نسخه PDF — نگارش استاندارد و علمی
  مشاهده نمونه نسخه نکات رسمی
• کتاب صدها پرسش و پاسخ تشریحی – نسخه PDF
  — هر سؤال همراه با پاسخ کامل برای درک عمیق مفاهیم
  مشاهده نمونه نسخه پرسش و پاسخ
• کتاب پرسش و پاسخ چهارگزینه‌ای – نسخه یادگیری سریع
  — پاسخ‌ها بلافاصله بعد از سؤال برای مرور سریع
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• کتاب پرسش و پاسخ چهارگزینه‌ای – نسخه خودآزمایی
  — پاسخ‌ها در انتهای بخش‌ها برای سنجش واقعی یادگیری
  مشاهده نمونه نسخه آزمونی

🎯 این بسته یک دورهٔ آموزشی کامل و چندلایه است؛ شامل ویدیوهای آموزشی، کتاب‌ها، تمرین‌ها و خودآزمایی.

چکیده

Whisper, as a form of speech, is not sufficiently addressed by mainstream speech applications. This is due to the fact that systems built for normal speech do not work as expected for whispered speech. A first step to building a speech application that is inclusive of whispered speech, is the successful classification of whispered speech and normal speech. Such a front-end classification system is expected to have high accuracy and low computational overhead, which is the scope of this paper. One of the characteristics of whispered speech is the absence of the fundamental frequency (or pitch), and hence the pitch harmonics as well. The presence of the pitch and pitch harmonics in normal speech, and its absence in whispered speech, is evident in the spectral envelope of the Fourier transform. We observe that this characteristic is predominant in the first quarter of the spectrum, and exploit the same as a feature. We propose the use of one dimensional convolutional neural networks (1D-CNN) to capture these features from the quartered spectral envelope (QSE). The system yields an accuracy of 99.31% when trained and tested on the wTIMIT dataset, and 100% on the CHAINS dataset. The proposed feature is compared with Mel frequency cepstral coefficients (MFCC), a staple in the speech domain. The proposed classification system is also compared with the state-of-the-art system based on log-filterbank energy (LFBE) features trained on long short-term memory (LSTM) network. The proposed system based on 1D-CNN performs better than, or as good as, the state-of-the-art across multiple experiments. It also converges sooner, with lesser computational overhead. Finally, the proposed system is evaluated under the presence of white noise at various signal-to-noise ratios and found to be robust.

چکیده به فارسی (ترجمه ماشینی)