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Register a new userBoosting Objective Scores of a Speech Enhancement Model by MetricGAN Post-processing
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The Transformer architecture has demonstrated a superior ability compared to recurrent neural networks in many different natural language processing applications. Therefore, our study applies a modified Transformer in a speech enhancement task. Specifically, positional encoding in the Transformer may not be necessary for speech enhancement, and hence, it is replaced by convolutional layers. To further improve the perceptual evaluation of the speech quality (PESQ) scores of enhanced speech, the L_1 pre-trained Transformer is fine-tuned using a MetricGAN framework. The proposed MetricGAN can be treated as a general post-processing module to further boost the objective scores of interest. The experiments were conducted using the data sets provided by the organizer of the Deep Noise Suppression (DNS) challenge. Experimental results demonstrated that the proposed system outperformed the challenge baseline, in both subjective and objective evaluations, with a large margin.
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Recent research on speech enhancement (SE) has seen the emergence of deep-learning-based methods. It is still a challenging task to determine the effective ways to increase the generalizability of SE under diverse test conditions. In this study, we combine zero-shot learning and ensemble learning to propose a zero-shot model selection (ZMOS) approach to increase the generalization of SE performance. The proposed approach is realized in the offline and online phases. The offline phase clusters the entire set of training data into multiple subsets and trains a specialized SE model (termed component SE model) with each subset. The online phase selects the most suitable component SE model to perform the enhancement. Furthermore, two selection strategies were developed: selection based on the quality score (QS) and selection based on the quality embedding (QE). Both QS and QE were obtained using a Quality-Net, a non-intrusive quality assessment network. Experimental results confirmed that the proposed ZMOS approach can achieve better performance in both seen and unseen noise types compared to the baseline systems and other model selection systems, which indicates the effectiveness of the proposed approach in providing robust SE performance.
Diffusion probabilistic models have demonstrated an outstanding capability to model natural images and raw audio waveforms through a paired diffusion and reverse processes. The unique property of the reverse process (namely, eliminating non-target signals from the Gaussian noise and noisy signals) could be utilized to restore clean signals. Based on this property, we propose a diffusion probabilistic model-based speech enhancement (DiffuSE) model that aims to recover clean speech signals from noisy signals. The fundamental architecture of the proposed DiffuSE model is similar to that of DiffWave--a high-quality audio waveform generation model that has a relatively low computational cost and footprint. To attain better enhancement performance, we designed an advanced reverse process, termed the supportive reverse process, which adds noisy speech in each time-step to the predicted speech. The experimental results show that DiffuSE yields performance that is comparable to related audio generative models on the standardized Voice Bank corpus SE task. Moreover, relative to the generally suggested full sampling schedule, the proposed supportive reverse process especially improved the fast sampling, taking few steps to yield better enhancement results over the conventional full step inference process.
Adversarial loss in a conditional generative adversarial network (GAN) is not designed to directly optimize evaluation metrics of a target task, and thus, may not always guide the generator in a GAN to generate data with improved metric scores. To overcome this issue, we propose a novel MetricGAN approach with an aim to optimize the generator with respect to one or multiple evaluation metrics. Moreover, based on MetricGAN, the metric scores of the generated data can also be arbitrarily specified by users. We tested the proposed MetricGAN on a speech enhancement task, which is particularly suitable to verify the proposed approach because there are multiple metrics measuring different aspects of speech signals. Moreover, these metrics are generally complex and could not be fully optimized by Lp or conventional adversarial losses.
We explore the possibility of leveraging accelerometer data to perform speech enhancement in very noisy conditions. Although it is possible to only partially reconstruct users speech from the accelerometer, the latter provides a strong conditioning signal that is not influenced from noise sources in the environment. Based on this observation, we feed a multi-modal input to SEANet (Sound EnhAncement Network), a wave-to-wave fully convolutional model, which adopts a combination of feature losses and adversarial losses to reconstruct an enhanced version of users speech. We trained our model with data collected by sensors mounted on an earbud and synthetically corrupted by adding different kinds of noise sources to the audio signal. Our experimental results demonstrate that it is possible to achieve very high quality results, even in the case of interfering speech at the same level of loudness. A sample of the output produced by our model is available at https://google-research.github.io/seanet/multimodal/speech.
In this study, we present a deep learning-based speech signal-processing mobile application, called CITISEN, which can perform three functions: speech enhancement (SE), model adaptation (MA), and acoustic scene conversion (ASC). For SE, CITISEN can effectively reduce noise components from speech signals and accordingly enhance their clarity and intelligibility. When it encounters noisy utterances with unknown speakers or noise types, the MA function allows CITISEN to effectively improve the SE performance by adapting an SE model with a few audio files. Finally, for ASC, CITISEN can convert the current background sound into a different background sound. The experimental results confirmed the effectiveness of performing SE, MA, and ASC functions via objective evaluation and subjective listening tests. Moreover, the MA experimental results indicated that short-time objective intelligibility (STOI) and perceptual evaluation of speech quality (PESQ) could be improved by approximately 5% and 10%, respectively. The promising results reveal that the developed CITISEN mobile application can be potentially used as a front-end processor for various speech-related services such as voice communication, assistive hearing devices, and virtual reality headsets. In addition, CITISEN can be used as a platform for using and evaluating the newly performed deep-learning-SE models, and can flexibly extend the models to address various noise environments and users.
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