An Efficient Optimal Reconstruction Based Speech Separation Based on Hybrid Deep Learning Technique

Conventional single-channel speech separation has two long-standing issues. The first issue, over-smoothing, is addressed, and estimated signals are used to expand the training data set. Second, DNN generates prior knowledge to address the problem of incomplete separation and mitigate speech distortion. To overcome all current issues, we suggest employing an efficient optimal reconstruction-based speech separation (ERSS) to overcome those problems using a hybrid deep learning technique. First, we propose an integral fox ride optimization (IFRO) algorithm for spectral structure reconstruction with the help of multiple spectrum features: time dynamic information, binaural and mono features. Second, we introduce a hybrid retrieval-based deep neural network (RDNN) to reconstruct the spectrograms size of speech and noise directly. The input signals are sent to Short Term Fourier Transform (STFT). STFT converts a clean input signal into spectrograms then uses a feature extraction technique called IFRO to extract features from spectrograms. After extracting the features, using the RDNN classification algorithm, the classified features are converted to softmax. ISTFT then applies to softmax and correctly separates speech signals. Experiments show that our proposed method achieves the highest gains in SDR, SIR, SAR STIO, and PESQ outcomes of 10.9, 15.3, 10.8, 0.08, and 0.58, respectively. The Joint-DNN-SNMF obtains 9.6, 13.4, 10.4, 0.07, and 0.50, comparable to the Joint-DNN-SNMF. The proposed result is compared to a different method and some previous work. In comparison to previous research, our proposed methodology yields better results

Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments

Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition that stills remains an important challenge. Data-driven supervised approaches, including ones based on deep neural networks, have recently emerged as potential alternatives to traditional unsupervised approaches and with sufficient training, can alleviate the shortcomings of the unsupervised methods in various real-life acoustic environments. In this light, we review recently developed, representative deep learning approaches for tackling non-stationary additive and convolutional degradation of speech with the aim of providing guidelines for those involved in the development of environmentally robust speech recognition systems. We separately discuss single- and multi-channel techniques developed for the front-end and back-end of speech recognition systems, as well as joint front-end and back-end training frameworks