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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/130588


    Title: 多層次極限學習機於語音訊號處理上的應用
    Hierarchical Extreme Learning Machine for Speech Signal Processing
    Authors: 胡泰克
    Hussain, Tassadaq
    Contributors: 曹昱
    廖文宏
    Yu Tsao
    Wen-Hung Liao
    胡泰克
    Tassadaq Hussain
    Keywords: 语音信号处理
    分层极端学习机
    公路极限学习机
    剩余的极限学习机
    渠道补偿
    多模式学习
    模型压缩
    Speech Signal Processing
    Hierarchical Extreme Learning Machines
    Highway Extreme Learning Machines
    Residual Extreme Learning Machines
    Channel Compensation
    Multimodal Learning
    Model Compression
    Date: 2020
    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。

    除了噪音，混響是另一個語音的問題。混響通常是指反射聲音的總集合，會嚴重影響與語音應用的效能。近年來，深層類神經模型強大的回歸能力已經證實可以有效地對語音去除混響效果。但是深層類神經模型有個重大缺點，就是需要大量的混響-無混響訓練語音對來訓練，而大量的訓練資料對通常並不容易取得。因此，開發一種使用少量的訓練數據的演算法變成重要的研究議題。本論文研究以HELM來解決了混響問題和數據需求問題，同時提出了利用整體學習框架的優點。實驗結果表明，在匹配以及不匹配的測試條件下，該框架優於傳統方法和最近提出的整體深度學習演算法。

    一個語音增強方法的局限是在沒見過的聲學條件下無法獲得令人滿意的性能。在本論文中，我們嘗試基於HELM解決通道不匹配的影響，可以在真實的聲學條件下將低質量的骨傳導麥克風話音轉換為高質量的空氣傳導麥克風話音。除了純音頻處理框架外，我們還將所提出的方法應用於多模態學習來改善純音頻語音增強模型的整體性能。在本論文中，我們也提出了一個結合聲音影像的語音增強系統。結果證實在不同的測試條件下，與僅有音頻的語音增強系統相比，結合聲音影像的語音增強系統可以提供更佳的效能。深度學習的另一個新興研究主題是促進模型壓縮以進一步增加應用性。我們提出了新穎的模型壓縮技術，可以有效地降低計算需求。未來我們預期壓縮後的模型能夠實現於硬體，並且與各種語音應用結合。
    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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