Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments (1705.10874v3)

Abstract: Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition that stills remains an important challenge. Data-driven supervised approaches, including ones based on deep neural networks, have recently emerged as potential alternatives to traditional unsupervised approaches and with sufficient training, can alleviate the shortcomings of the unsupervised methods in various real-life acoustic environments. In this light, we review recently developed, representative deep learning approaches for tackling non-stationary additive and convolutional degradation of speech with the aim of providing guidelines for those involved in the development of environmentally robust speech recognition systems. We separately discuss single- and multi-channel techniques developed for the front-end and back-end of speech recognition systems, as well as joint front-end and back-end training frameworks.

• The paper categorizes deep learning methods into front-end, back-end, and joint training frameworks to enhance ASR robustness in noisy settings.
• It analyzes mapping-based and masking-based front-end techniques while exploring architectures like CNNs, LSTMs, and GANs for noise filtering.
• The study highlights multi-channel processing with beamformers and suggests future directions for end-to-end raw audio processing.

Overview of "Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments"

The paper "Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments" offers a detailed examination of recent advancements in leveraging deep learning techniques to improve the robustness of Automatic Speech Recognition (ASR) systems in noisy environments. The paper assesses the efficacy of data-driven supervised approaches as alternatives to traditional unsupervised methods that have historically struggled with non-stationary acoustic interferences, particularly in real-life settings.

Summary of Key Contributions

1. Categorization of Deep Learning Approaches: The paper presents a taxonomy of deep learning methods differentiated by the number of processing channels they address—single-channel or multi-channel—and further segmenting them by the ASR system processing stage they are applied to—front-end, back-end, or joint front- and back-end.
2. Detailed Analysis of Front-End Techniques: Emphasizing the significance of feature representation, the paper reviews two primary categories of front-end techniques: mapping-based methods, which directly reconstruct clean speech features from noisy observations, and masking-based methods, which estimate masks to filter noise in complex signal environments. The application of architectures such as DNNs, CNNs, LSTM-RNNs, and even GANs in these frameworks is explored.
3. Back-End Innovations: The paper discusses various training and adaptation strategies for acoustic models, traditional GMM-HMM structures, and modern DNN-based alternatives. Key strategies reviewed include multi-condition training, model adaptation, noise-aware training, and multi-task learning, demonstrating the flexibility and adaptability required to handle environmental variability.
4. Advances in Joint Training Approaches: By merging front-end enhancement and back-end recognition, joint training frameworks are highlighted for their capacity to integrate enhancement and recognition in a mutually beneficial manner. Such coupled architectures prove essential in mitigating the distinct optimization goals inherent in separate component structures.
5. Exploration of Multi-Channel Processing: Recognizing the value of microphone arrays, the paper examines neural network-supported beamforming techniques, including MVDR and GEV beamformers. It also investigates multi-channel joint front- and back-end approaches through convolutional and recurrent neural network pipelines for robust spatial signal processing.

Implications and Future Directions

The development of deep learning approaches has markedly improved the robustness of ASR systems amidst non-stationary noise, yet challenges persist. Future research may focus on further integrating phase-sensitive approaches, refining adversarial training techniques, and extending comprehensive end-to-end frameworks that directly process raw audio inputs. With the convergence of large-scale training data, sophisticated network architectures, and advancing computational resources, the future holds significant potential to close the gap between ASR performance in controlled and wild environments.

The paper anchors its value in providing a structured overview of state-of-the-art paradigms, underpinning complex noise-robust ASR challenges with empirical insights and future research trajectories. It provides both a foundation and a forward-looking lens for researchers aiming to harness deep learning in tackling environmental robustness for speech systems.