Incorporating Ultrasound Tongue Images for Audio-Visual Speech Enhancement through Knowledge Distillation

Audio-visual speech enhancement (AV-SE) aims to enhance degraded speech along with extra visual information such as lip videos, and has been shown to be more effective than audio-only speech enhancement. This paper proposes further incorporating ultrasound tongue images to improve lip-based AV-SE systems' performance. Knowledge distillation is employed at the training stage to address the challenge of acquiring ultrasound tongue images during inference, enabling an audio-lip speech enhancement student model to learn from a pre-trained audio-lip-tongue speech enhancement teacher model. Experimental results demonstrate significant improvements in the quality and intelligibility of the speech enhanced by the proposed method compared to the traditional audio-lip speech enhancement baselines. Further analysis using phone error rates (PER) of automatic speech recognition (ASR) shows that palatal and velar consonants benefit most from the introduction of ultrasound tongue images.Comment: To be published in InterSpeech 202

Incorporating Ultrasound Tongue Images for Audio-Visual Speech Enhancement

Audio-visual speech enhancement (AV-SE) aims to enhance degraded speech along with extra visual information such as lip videos, and has been shown to be more effective than audio-only speech enhancement. This paper proposes the incorporation of ultrasound tongue images to improve the performance of lip-based AV-SE systems further. To address the challenge of acquiring ultrasound tongue images during inference, we first propose to employ knowledge distillation during training to investigate the feasibility of leveraging tongue-related information without directly inputting ultrasound tongue images. Specifically, we guide an audio-lip speech enhancement student model to learn from a pre-trained audio-lip-tongue speech enhancement teacher model, thus transferring tongue-related knowledge. To better model the alignment between the lip and tongue modalities, we further propose the introduction of a lip-tongue key-value memory network into the AV-SE model. This network enables the retrieval of tongue features based on readily available lip features, thereby assisting the subsequent speech enhancement task. Experimental results demonstrate that both methods significantly improve the quality and intelligibility of the enhanced speech compared to traditional lip-based AV-SE baselines. Moreover, both proposed methods exhibit strong generalization performance on unseen speakers and in the presence of unseen noises. Furthermore, phone error rate (PER) analysis of automatic speech recognition (ASR) reveals that while all phonemes benefit from introducing ultrasound tongue images, palatal and velar consonants benefit most.Comment: Submmited to IEEE/ACM Transactions on Audio, Speech and Language Processing. arXiv admin note: text overlap with arXiv:2305.1493

Single-Microphone Speech Enhancement and Separation Using Deep Learning

The cocktail party problem comprises the challenging task of understanding a speech signal in a complex acoustic environment, where multiple speakers and background noise signals simultaneously interfere with the speech signal of interest. A signal processing algorithm that can effectively increase the speech intelligibility and quality of speech signals in such complicated acoustic situations is highly desirable. Especially for applications involving mobile communication devices and hearing assistive devices. Due to the re-emergence of machine learning techniques, today, known as deep learning, the challenges involved with such algorithms might be overcome. In this PhD thesis, we study and develop deep learning-based techniques for two sub-disciplines of the cocktail party problem: single-microphone speech enhancement and single-microphone multi-talker speech separation. Specifically, we conduct in-depth empirical analysis of the generalizability capability of modern deep learning-based single-microphone speech enhancement algorithms. We show that performance of such algorithms is closely linked to the training data, and good generalizability can be achieved with carefully designed training data. Furthermore, we propose uPIT, a deep learning-based algorithm for single-microphone speech separation and we report state-of-the-art results on a speaker-independent multi-talker speech separation task. Additionally, we show that uPIT works well for joint speech separation and enhancement without explicit prior knowledge about the noise type or number of speakers. Finally, we show that deep learning-based speech enhancement algorithms designed to minimize the classical short-time spectral amplitude mean squared error leads to enhanced speech signals which are essentially optimal in terms of STOI, a state-of-the-art speech intelligibility estimator.Comment: PhD Thesis. 233 page

잡음에 강인한 음성 구간 검출과 음성 향상을 위한 딥 러닝 기반 기법 연구

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2017. 2. 김남수.Over the past decades, a number of approaches have been proposed to improve the performances of voice activity detection (VAD) and speech enhancement algorithms which are crucial for speech communication and speech signal processing systems. In particular, the increasing use of machine learning-based techniques has led to the more robust algorithms in low SNR conditions. Among them, the deep neural network (DNN) has been one of the most popular techniques. While the DNN-based technique is successfully applied to these tasks, the characteristics of VAD and speech enhancement tasks are not fully incorporated to the DNN structures and objective functions. In this thesis, we propose the novel training schemes and post-filter for DNN-based VAD and speech enhancement. Unlike algorithms with basic DNN-based framework, the proposed algorithm combines the knowledge from signal processing and machine learning society to develop the improve DNN-based VAD and speech enhancement algorithm. In the following chapters, the environmental mismatch problem in the VAD area is compensated by applying multi-task learning to the DNN-based VAD. Also, the DNN-based framework is proposed in the speech enhancement scenario and the novel objective function and post-filter which are derived from the characteristics on human auditory perception improve the DNN-based speech enhancement algorithm. In the VAD task, the DNN-based algorithm was recently proposed and outperformed the traditional and other machine learning-based VAD algorithms. However, the performance of the DNN-based algorithm sometimes deteriorates when the training and test environments are not matched with each other. In order to increase the performance of the DNN-based VAD in unseen environments, we adopt the multi-task learning (MTL) framework which consists of the primary VAD and subsidiary feature enhancement tasks. By employing the MTL framework, the DNN learns the denoising function in the shared hidden layers that is useful to maintain the VAD performance in mismatched noise conditions. Second, the DNN-based framework is applied to the speech enhancement by considering it as a regression task. The encoding vector of the conventional nonnegative matrix factorization (NMF)-based algorithm is estimated by the proposed DNN and the performance of the DNN-based algorithm is compared to the conventional NMF-based algorithm. Third, the perceptually motivated objective function is proposed for the DNN-based speech enhancement. In the proposed technique, a new objective function which consists of the Mel-scale weighted mean square error, temporal and spectral variations similarities between the enhanced and clean speech is employed in the DNN training stage. The proposed objective function helps to compute the gradients based on a perceptually motivated non-linear frequency scale and alleviates the over-smoothness of the estimated speech. Furthermore, the post-filter which adjusts the variance over frequency bins further compensates the lack of contrasts between spectral peaks and valleys in the enhanced speech. The conventional GV equalization post-filters do not consider the spectral dynamics over frequency bins. To consider the contrast between spectral peaks and valleys in each enhanced speech frames, the proposed algorithm matches the variance over coefficients in the log-power spectra domain. Finally, in the speech enhancement task, an integrated technique using the proposed perceptually motivated objective function and the post-filter is described. In matched and mismatched noise conditions, the performance results of the conventional and proposed algorithm are discussed. Also, the subjective preference test result of these algorithms is also provided.1 Introduction 1 2 Conventional Approaches for Speech Enhancement 7 2.1 NMF-Based Speech Enhancement 7 3 Deep Neural Networks 13 3.1 Introduction 13 3.2 Objective Function 14 3.3 Stochastic Gradient Descent 16 4 DNN-Based Voiced Activity Detection with Multi-Task Learning Framework 19 4.1 Introduction 19 4.2 DNN-Based VAD Algorithm 21 4.3 DNN-Based VAD with MTL framework 23 4.4 Experimental Results 26 4.4.1 Experiments in Matched Noise Conditions 26 4.4.2 Experiments in Mismatched Noise Conditions 28 4.5 Summary 30 5 NMF-based Speech Enhancement Using Deep Neural Network 35 5.1 Introduction 35 5.2 Encoding Vector Estimation Using DNN 37 5.3 Experiments 42 5.4 Summary 47 6 DNN-Based Monaural Speech Enhancement with Temporal and Spectral Variations Equalization 49 6.1 Introduction 49 6.2 Conventional DNN-Based Speech Enhancement 53 6.2.1 Training Stage 53 6.2.2 Test Stage 55 6.3 Perceptually-Motivated Criteria 56 6.3.1 Perceptually Motivated Objective Function 56 6.3.2 Mel-Scale Weighted Mean Square Error 58 6.3.3 Temporal Variation Similarity 58 6.3.4 Spectral Variation Similarity 61 6.3.5 DNN Training with the Proposed Objective Function 62 6.4 Experiments 62 6.4.1 Performance Evaluation with Varying Weight Parameters 64 6.4.2 Performance Evaluation in Matched Noise Conditions 64 6.4.3 Performance Evaluation in Mismatched Noise Conditions 66 6.4.4 Comparison Between Variation Analysis Method 66 6.4.5 Subjective Test Results 67 6.5 Summary 68 7 Spectral Variance Equalization Post-filter for DNN-Based Speech Enhancement 75 7.1 Introduction 75 7.2 GV Equalization Post-Filter 76 7.3 Spectral Variance(SV) Equalization Post-Filter 77 7.4 Experiments 78 7.4.1 Objective Test Results 78 7.4.2 Subjective Test Results 79 7.5 Summary 81 8 Conclusions 83 Bibliography 85 Appendix 95 요약 97Docto

Sub-Band Knowledge Distillation Framework for Speech Enhancement

In single-channel speech enhancement, methods based on full-band spectral features have been widely studied. However, only a few methods pay attention to non-full-band spectral features. In this paper, we explore a knowledge distillation framework based on sub-band spectral mapping for single-channel speech enhancement. Specifically, we divide the full frequency band into multiple sub-bands and pre-train an elite-level sub-band enhancement model (teacher model) for each sub-band. These teacher models are dedicated to processing their own sub-bands. Next, under the teacher models' guidance, we train a general sub-band enhancement model (student model) that works for all sub-bands. Without increasing the number of model parameters and computational complexity, the student model's performance is further improved. To evaluate our proposed method, we conducted a large number of experiments on an open-source data set. The final experimental results show that the guidance from the elite-level teacher models dramatically improves the student model's performance, which exceeds the full-band model by employing fewer parameters.Comment: Published in Interspeech 202

SNR-Based Teachers-Student Technique for Speech Enhancement

It is very challenging for speech enhancement methods to achieves robust performance under both high signal-to-noise ratio (SNR) and low SNR simultaneously. In this paper, we propose a method that integrates an SNR-based teachers-student technique and time-domain U-Net to deal with this problem. Specifically, this method consists of multiple teacher models and a student model. We first train the teacher models under multiple small-range SNRs that do not coincide with each other so that they can perform speech enhancement well within the specific SNR range. Then, we choose different teacher models to supervise the training of the student model according to the SNR of the training data. Eventually, the student model can perform speech enhancement under both high SNR and low SNR. To evaluate the proposed method, we constructed a dataset with an SNR ranging from -20dB to 20dB based on the public dataset. We experimentally analyzed the effectiveness of the SNR-based teachers-student technique and compared the proposed method with several state-of-the-art methods.Comment: Published in 2020 IEEE International Conference on Multimedia and Expo (ICME 2020