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Medical Instrumentation Application And Design

Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition but still remains an important challenge. Data-driven supervised approaches, especially the 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. In the meanwhile, we discuss the pros and cons of these approaches and provide their experimental results on benchmark databases. We expect that this overview can facilitate the development of the robustness of speech recognition systems in acoustic noisy environments.

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

The Hearing-Aid Speech Perception Index (HASPI)

As speech processing devices like mobile phones, voice controlled devices, and hearing aids have increased in popularity, people expect them to work anywhere and at any time without user intervention However, the presence of acoustical disturbances limits the use of these applications, degrades their performance, or causes the user difficulties in understanding the conversation or appreciating the device A common way to reduce the effects of such disturbances is through the use of single-microphone noise reduction algorithms for speech enhancement The field of single-microphone noise reduction for speech enhancement comprises a history of more than 30 years of research In this survey, we wish to demonstrate the significant advances that have been made during the last decade in the field of discrete Fourier transform domain-based single-channel noise reduction for speech enhancementFurthermore, our goal is to provide a concise description of a state-of-the-art speech enhancement system, and demonstrate the relative importance of the various building blocks of such a system This allows the non-expert DSP practitioner to judge the relevance of each building block and to implement a close-to-optimal enhancement system for the particular application at hand Table of Contents: Introduction / Single Channel Speech Enhancement: General Principles / DFT-Based Speech Enhancement Methods: Signal Model and Notation / Speech DFT Estimators / Speech Presence Probability Estimation / Noise PSD Estimation / Speech PSD Estimation / Performance Evaluation Methods / Simulation Experiments with Single-Channel Enhancement Systems / Future Directions

DFT-Domain Based Single-Microphone Noise Reduction for Speech Enhancement: A Survey of the State of the Art

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.

https://maxwell.ict.griffith.edu.au/spl/publications/papers/spcom10_modspsub.pdf

Single-channel speech enhancement using spectral subtraction in the short-time modulation domain

https://maxwell.ict.griffith.edu.au/spl/publications/papers/spcom12_modmmse.pdf

Speech enhancement using a minimum mean-square error short-time spectral modulation magnitude estimator

https://maxwell.ict.griffith.edu.au/spl/publications/papers/spcom11_kkp_modint.pdf

Role of modulation magnitude and phase spectrum towards speech intelligibility

https://maxwell.ict.griffith.edu.au/spl/publications/papers/spcom14_qsti.pdf

Belinda Schwerin

Papers

Single-channel speech enhancement using spectral subtraction in the short-time modulation domain

Speech enhancement using a minimum mean-square error short-time spectral modulation magnitude estimator

Role of modulation magnitude and phase spectrum towards speech intelligibility

An improved speech transmission index for intelligibility prediction

Using STFT real and imaginary parts of modulation signals for MMSE-based speech enhancement