Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userSpeech Enhancement Using Multi-Stage Self-Attentive Temporal Convolutional Networks
210
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Multi-stage learning is an effective technique to invoke multiple deep-learning modules sequentially. This paper applies multi-stage learning to speech enhancement by using a multi-stage structure, where each stage comprises a self-attention (SA) block followed by stacks of temporal convolutional network (TCN) blocks with doubling dilation factors. Each stage generates a prediction that is refined in a subsequent stage. A fusion block is inserted at the input of later stages to re-inject original information. The resulting multi-stage speech enhancement system, in short, multi-stage SA-TCN, is compared with state-of-the-art deep-learning speech enhancement methods using the LibriSpeech and VCTK data sets. The multi-stage SA-TCN systems hyper-parameters are fine-tuned, and the impact of the SA block, the fusion block and the number of stages are determined. The use of a multi-stage SA-TCN system as a front-end for automatic speech recognition systems is investigated as well. It is shown that the multi-stage SA-TCN systems perform well relative to other state-of-the-art systems in terms of speech enhancement and speech recognition scores.
rate research
Read More
Deep learning has achieved substantial improvement on single-channel speech enhancement tasks. However, the performance of multi-layer perceptions (MLPs)-based methods is limited by the ability to capture the long-term effective history information. The recurrent neural networks (RNNs), e.g., long short-term memory (LSTM) model, are able to capture the long-term temporal dependencies, but come with the issues of the high latency and the complexity of training.To address these issues, the temporal convolutional network (TCN) was proposed to replace the RNNs in various sequence modeling tasks. In this paper we propose a novel TCN model that employs multi-branch structure, called multi-branch TCN (MB-TCN), for monaural speech enhancement.The MB-TCN exploits split-transform-aggregate design, which is expected to obtain strong representational power at a low computational complexity.Inspired by the TCN, the MB-TCN model incorporates one dimensional causal dilated CNN and residual learning to expand receptive fields for capturing long-term temporal contextual information.Our extensive experimental investigation suggests that the MB-TCNs outperform the residual long short-term memory networks (ResLSTMs), temporal convolutional networks (TCNs), and the CNN networks that employ dense aggregations in terms of speech intelligibility and quality, while providing superior parameter efficiency. Furthermore, our experimental results demonstrate that our proposed MB-TCN model is able to outperform multiple state-of-the-art deep learning-based speech enhancement methods in terms of five widely used objective metrics.
The intelligibility of speech severely degrades in the presence of environmental noise and reverberation. In this paper, we propose a novel deep learning based system for modifying the speech signal to increase its intelligibility under the equal-power constraint, i.e., signal power before and after modification must be the same. To achieve this, we use generative adversarial networks (GANs) to obtain time-frequency dependent amplification factors, which are then applied to the input raw speech to reallocate the speech energy. Instead of optimizing only a single, simple metric, we train a deep neural network (DNN) model to simultaneously optimize multiple advanced speech metrics, including both intelligibility- and quality-related ones, which results in notable improvements in performance and robustness. Our system can not only work in non-realtime mode for offline audio playback but also support practical real-time speech applications. Experimental results using both objective measurements and subjective listening tests indicate that the proposed system significantly outperforms state-ofthe-art baseline systems under various noisy and reverberant listening conditions.
Supervised learning for single-channel speech enhancement requires carefully labeled training examples where the noisy mixture is input into the network and the network is trained to produce an output close to the ideal target. To relax the conditions on the training data, we consider the task of training speech enhancement networks in a self-supervised manner. We first use a limited training set of clean speech sounds and learn a latent representation by autoencoding on their magnitude spectrograms. We then autoencode on speech mixtures recorded in noisy environments and train the resulting autoencoder to share a latent representation with the clean examples. We show that using this training schema, we can now map noisy speech to its clean version using a network that is autonomously trainable without requiring labeled training examples or human intervention.
We propose to implement speech enhancement by the regeneration of clean speech from a salient representation extracted from the noisy signal. The network that extracts salient features is trained using a set of weight-sharing clones of the extractor network. The clones receive mel-frequency spectra of different noi
Previous studies have proven that integrating video signals, as a complementary modality, can facilitate improved performance for speech enhancement (SE). However, video clips usually contain large amounts of data and pose a high cost in terms of computational resources and thus may complicate the SE system. As an alternative source, a bone-conducted speech signal has a moderate data size while manifesting speech-phoneme structures, and thus complements its air-conducted counterpart. In this study, we propose a novel multi-modal SE structure in the time domain that leverages bone- and air-conducted signals. In addition, we examine two ensemble-learning-based strategies, early fusion (EF) and late fusion (LF), to integrate the two types of speech signals, and adopt a deep learning-based fully convolutional network to conduct the enhancement. The experiment results on the Mandarin corpus indicate that this newly presented multi-modal (integrating bone- and air-conducted signals) SE structure significantly outperforms the single-source SE counterparts (with a bone- or air-conducted signal only) in various speech evaluation metrics. In addition, the adoption of an LF strategy other than an EF in this novel SE multi-modal structure achieves better results.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
