Howtosignalprocessing
- Is Deep learning used for speech recognition?
- Can neural networks be used for speech recognition?
- What are the speech enhancement techniques?
- Can we use RNN for speech recognition?
Is Deep learning used for speech recognition?
In the deep learning era, neural networks have shown significant improvement in the speech recognition task. Various methods have been applied such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), while recently Transformer networks have achieved great performance.
Can neural networks be used for speech recognition?
Neural networks are very powerful for recognition of speech. There are various networks for this process. RNN, LSTM, Deep Neural network and hybrid HMM-LSTM are used for speech recognition.
What are the speech enhancement techniques?
A number of speech enhancement techniques have been reported in the literature [32]. They include spectral subtraction [33, 34, 41], Wiener and Kalman filtering [35], MMSE estimation [36], comb filtering [32], subspace methods [37, 38], and phase spectrum compensation [39, 40].
Can we use RNN for speech recognition?
RNN seems to be more natural for speech recognition than MLP because it allows variability in input length [17]. The motivation for applying recurrent neural network to this domain is to take advantage of their ability to process short-term spectral features but yet respond to long-term temporal events.
