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Hierarchical Extreme Learning Machine for Speech Signal Processing
Speech Signal Processing
Hierarchical Extreme Learning Machines
Highway Extreme Learning Machines
Residual Extreme Learning Machines
|Issue Date: ||2020-07-01 13:49:55 (UTC+8)|
|Abstract: ||語音是人與人互動中最有效、最自然的手段，在過去的幾十年中，語音信號處理的各個議題已經被深入地研究，然而在真實聲學環境下有效提高人類聽覺、機器識別率仍然是一項艱鉅的任務。近年來以語音控制的個人助理系統（例如Alexa、Google Home等）已經被大幅使用，進而重塑了人機交互模式。在經常需要遠距離交談的實際應用中（例如，音頻數據挖掘和語音輔助應用），背景噪聲會嚴重降低語音信號的質量和清晰度，因此，能夠抑制噪聲是在實用環境下的重要議題。針對這個議題，本文首先提出了一種語音去噪框架，其目的是：（i）有效、快速地從單通道語音信號中去除背景噪聲；（ii）在不匹配測試條件下（靜態和非靜態噪聲以及不同SNR級別），能夠有效地從嘈雜的聲音中提取出清晰的語音特徵。（iii）在訓練數據量有限的情況下也可以獲得優異的除噪性能。實驗結果證實與基於深層類神經網絡的方法相比，在訓練數據量有限的情況下，所提出的HELM框架可以產生效果相當甚至更好的語音品質和清晰度。|
Speech is the most effective and natural medium of communication in human–human interaction. In the past few decades, a great amount of research has been conducted on various aspects and properties of speech signal processing. However, improving the intelligibility for both human listening and machine recognition in real acoustic conditions still remains a challenging task. In recent years, voice-controlled personal assistants systems (such as Alexa, Google Home, and Home Pod, etc.) have been widely used, and have reshaped the human-machine interaction mode. In practical applications that often require distant talking communications (e.g., audio data mining and voice-assisted applications), the effect of background noise can severely deteriorate the quality and intelligibility of speech signals for both human and machine listeners. Therefore, it is desirable that noise suppression can be made robust against changing noise conditions to operate in real-time environments. To address this issue, this dissertation initially presents a speech denoising framework which aims, (i) at the effective and fast removal of background noise from a single-channel speech signal, (ii) to extract clean speech features from the noisy counterpart and effective even under mismatch testing conditions (stationary and non-stationary noise and SNR levels), and (iii) to attain optimal performance when the amount of training data is limited. The proposed framework offers a universal approximation capability through comparative measures. The experimental results demonstrate that the proposed framework can yield comparable or even better speech quality and intelligibility compared with conventional signal processing- and deep neural-based approaches when the amount of training data is limited.
Besides noise, reverberation is yet another issue that can affect the learning effectiveness and robustness of distant-talking communication devices. Reverberation generally refers to the collection of reflected sounds that can affect the performance of speech-related applications significantly. In recent years, the approximation capabilities of deeper neural models have been exploited to study the reverberation effect. The outcome of these studies indicate that neural-based learning have strong regression capabilities, and can substantially achieve outstanding speech dereverberation results. However, deep neural models require a large amount of reverberant-anechoic training waveform pairs to achieve reasonable performance improvement. Therefore, it is required to develop a data-driven solution that can achieve robust generalization performance for realistic reverberated conditions and can be optimized with a small amount of training data, or more precisely adaptation data. Motivated by the promising performance achieved for speech denoising, this dissertation next addresses the reverberation and data requirement issue while preserving the advantages of deep neural structures leveraging upon ensemble learning framework. Experimental results reveal that the proposed framework outperforms both traditional methods and a recently proposed integrated deep and ensemble learning algorithm in terms of standardized evaluation metrics under matched and mismatched testing conditions.
A common drawback of most modern speech enhancement (SE) approaches is that they are typically evaluated using simulated datasets, where training and testing conditions are generated in controlled environments. Consequently, these approaches suffer from channel mismatch problems in unseen acoustic conditions and are unable to achieve satisfactory performance. In online learning, where data arrives from different channels and environments, an effective solution is required to address the channel mismatch problem. In this dissertation, we will next address the impact of channel mismatch and propose an alternative SE system which converts low-quality bone-conducted microphone utterances into high-quality air-conducted microphone utterances in real acoustic conditions.
Although the effects of noise and reverberation using audio-only frameworks are well examined under diverse sets of synthetically generated conditions, such frameworks need to initially acquire a large number of training data, covering as many environmental conditions as possible, to improve the robustness against unknown test conditions. Recent literature has exploited the great potential of auxiliary information in human-machine interactions. The data obtained from heterogeneous sensors and devices using the internet of things (IoT) can be useful for more robust inference, thereby providing further insights into multimodal learning. In addition to audio-only SE frameworks, multimodal learning has recently been adopted to improve the overall performances of audio-only SE models. The thesis later expands the audio-only paradigm of the SE framework and proposes an audio-visual SE system. The final results demonstrate that the incorporation of auxiliary information alongside audio can provide adequate performance enhancement over an audio-only SE system under different test conditions.
Another emerging focus of deep learning is to facilitate deep neural-based models to work in real-world applications. The problem with the existing deep neural models is that they are computationally expensive and memory intensive, thereby limiting the deployment in edge devices with low memory resources. Based on the successful results of audio-only and audio-visual SE frameworks, in this thesis, we propose a joint audio-visual SE framework to finally address model and data compression strategies in order to meet the computational demands and facilitate real-time predictions. The proposed framework demonstrates that incorporation of visual information helps the framework to retain most of the information lost by the audio-only framework, while the model compression lets the framework to further reduce the computation requirement. The model compression enables the model to land in the hardware implementation arena for multimodal environments to obtain efficient regression ability.
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