In this paper, we considered the problem of the speech enhancement similar to the real-world environments where several complex noise sources simultaneously degrade the quality and intelligibility of a target speech. The existing literature on the speech enhancement principally focuses on the presence of one noise source in mixture signals. However, in real-world situations, we generally face and attempt to improve the quality and intelligibility of speech where various complex stationary and nonstationary noise sources are simultaneously mixed with the target speech. Here, we have used deep learning for speech enhancement in complex-noisy environments and used ideal binary mask (IBM) as a binary classification function by using deep neural networks (DNNs). IBM is used as a target function during training and the trained DNNs are used to estimate IBM during enhancement stage. The estimated target function is then applied to the complex-noisy mixtures to obtain the target speech. The mean square error (MSE) is used as an objective cost function at various epochs. The experimental results at different input signal-to-noise ratio (SNR) showed that DNN-based complex-noisy speech enhancement outperformed the competing methods in terms of speech quality by using perceptual evaluation of speech quality (PESQ), segmental signal-to-noise ratio (SNRSeg), log-likelihood ratio (LLR), weighted spectral slope (WSS). Moreover, short-time objective intelligibility (STOI) reinforced the better speech intelligibility.

Human speech in real-world environments is typically degraded by the background noise. They have a negative impact on perceptual speech quality and intelligibility which causes performance degradation in various speech-related technological applications, such as hearing aids and automatic speech recognition systems. It also degrades the original phase of the clean speech and introduces perceptual disturbance which leads to the negative impacts on the quality of speech. Therefore, speech enhancement must vigilantly be dealt with in everyday listening environments. In this paper, speech enhancement is performed using supervised learning of spectral masking. Deep neural networks (DNN) and recurrent neural networks (RNN) are trained to learn the spectral masking from the magnitude spectrograms of the degraded speech. An iterative procedure is adopted as a post-processing step to deal with the noisy phase. Additionally, an intelligibility improvement filter is also used to incorporate the critical band importance function weights where higher weights contribute more towards intelligibility. Systematic experiments demonstrated that the proposed approaches greatly attenuated the background noise. Also, they led to large improvements of the perceived speech quality and intelligibility, as well as automatic speech recognition. In experiments, TIMIT database is used. The STOI is improved by 17.6% over the noisy speech. Also, SDR and PESQ are improved by 5.22dB and 19% over the noisy speech utterances. These comparisons showed that the proposed speech enhancement approaches outperformed the related speech enhancement methods.

Speech enhancement (SE) is a critical aspect of various speech-processing applications. Recent research in this field focuses on identifying effective ways to capture the long-term contextual dependencies of speech signals to enhance performance. Deep convolutional networks (DCN) using self-attention and the Transformer model have demonstrated competitive results in SE. Transformer models with convolution layers can capture short and long-term temporal sequences by leveraging multi-head self-attention, which allows the model to attend the entire sequence. This study proposes a neural speech enhancement (NSE) using the convolutional encoder-decoder (CED) and convolutional attention Transformer (CAT), named the NSE-CATNet. To effectively process the time-frequency (T-F) distribution of spectral components in speech signals, a T-F attention module is incorporated into the convolutional Transformer model. This module enables the model to explicitly leverage position information and generate a two-dimensional attention map for the time-frequency speech distribution. The performance of the proposed SE is evaluated using objective speech quality and intelligibility metrics on two different datasets, the VoiceBank-DEMAND Corpus and the LibriSpeech dataset. The experimental results indicate that the proposed SE outperformed the competitive baselines in terms of speech enhancement performance at - 5dB, 0dB, and 5dB. This suggests that the model is effective at improving the overall quality by 0.704 with VoiceBank-DEMAND and by 0.692 with LibriSpeech. Further, the intelligibility with VoiceBank-DEMAND and LibriSpeech is improved by 11.325% and 11.75% over the noisy speech signals.