Editing State-of-the-art (section)

==== Vinith Kishore, Nitya Tiwari, and Periyasamy Paramasivam. “Improved Speech Enhancement Using TCN with Multiple Encoder-Decoder Layers”. In: Interspeech 2020. ISCA. 2020, pp. 4531–4535. doi: 10.21437/Interspeech.2020-3122. url: <nowiki>https://doi.org/10.21437/Interspeech.2020-3122</nowiki>. ====
* '''Abstract:''' This paper presents a deep learning-based single-channel speech enhancement technique that utilizes a multilayer encoder-decoder structure and a Temporal Convolutional Network (TCN) to improve the quality of speech for applications such as smart speakers and voice assistants. The technique leverages the encoder-decoder to obtain a representation suitable for speech enhancement and employs a TCN-based separator between the encoder and decoder to learn long-range dependencies. The optimal number of encoder-decoder layers is determined through t-SNE analysis of the representations learned by different architectures. Experimental results show that the proposed two-layer encoder-decoder structure achieved a 48% improvement in Word Error Rate (WER) over unprocessed noisy data and improvements of 33% and 44% in WER over two baseline models.
* '''Research Question (RQ):''' The research question focuses on exploring the effectiveness of the multilayer encoder-decoder structure in the task of single-channel speech enhancement and the role of TCN in learning long-range dependencies for separating noise and clean speech. Additionally, the study aims to determine the optimal number of encoder-decoder layers for effective noise suppression and speech enhancement.
* '''Hypothesis:''' The paper hypothesizes that using a multilayer encoder-decoder structure can obtain a noise-independent representation, which is useful for separating clean speech and noise. It is also hypothesized that TCN can effectively learn long-range dependencies in the encoded output and provide an enhanced speech mask, thereby improving the performance of speech enhancement.
* '''Conclusion:''' The conclusion indicates that the proposed two-layer encoder-decoder structure outperforms unprocessed noisy data and two baseline models in objective measures of speech quality (such as PESQ and SI-SNR) and Word Error Rate (WER) on a speech recognition platform. Furthermore, t-SNE analysis demonstrates that the two-layer structure can learn a representation suitable for speech enhancement applications.
* '''Critical Observation:''' Although the proposed architecture has achieved significant improvements in speech enhancement, the study mainly focuses on specific types of noise and speech datasets, which may not fully represent the diverse noise conditions in the real world. Moreover, increasing the number of encoder-decoder layers could lead to an increase in the number of model parameters, thereby increasing computational costs and the risk of overfitting. Future work needs to explore model optimization and compression techniques to reduce the number of parameters and test the generalizability and suitability of the technique in unseen noisy environments.

* '''Relevance :''' The research is closely related to the field of speech enhancement, especially in improving the performance of Automatic Speech Recognition (ASR) systems in noisy environments. By processing signals directly in the time domain using deep learning techniques, the study provides a new perspective and approach for designing effective single-channel speech enhancement systems. Additionally, by comparing the performance of different architectures, this paper offers guidance for selecting the appropriate model structure and number of layers, which is significant for developing efficient and accurate speech enhancement algorithms.