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Found 8 papers, 0 papers with code
On Speech Pre-emphasis as a Simple and Inexpensive Method to Boost Speech Enhancement
Pre-emphasis filtering, compensating for the natural energy decay of speech at higher frequencies, has been considered as a common pre-processing step in a number of speech processing tasks over the years.
Improved Vocal Effort Transfer Vector Estimation for Vocal Effort-Robust Speaker Verification
Despite the maturity of modern speaker verification technology, its performance still significantly degrades when facing non-neutrally-phonated (e. g., shouted and whispered) speech.
Filterbank Learning for Noise-Robust Small-Footprint Keyword Spotting
In the context of keyword spotting (KWS), the replacement of handcrafted speech features by learnable features has not yielded superior KWS performance.
Deep Spoken Keyword Spotting: An Overview
Spoken keyword spotting (KWS) deals with the identification of keywords in audio streams and has become a fast-growing technology thanks to the paradigm shift introduced by deep learning a few years ago.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Shouted Speech Compensation for Speaker Verification Robust to Vocal Effort Conditions
These compensation techniques are borrowed from the area of robustness for automatic speech recognition and, in this work, we apply them to compensate the mismatch between shouted and normal conditions in speaker verification.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Exploring Filterbank Learning for Keyword Spotting
Despite their great performance over the years, handcrafted speech features are not necessarily optimal for any particular speech application.
End-to-End Deep Residual Learning with Dilated Convolutions for Myocardial Infarction Detection and Localization
In this report, I investigate the use of end-to-end deep residual learning with dilated convolutions for myocardial infarction (MI) detection and localization from electrocardiogram (ECG) signals.
Keyword Spotting for Hearing Assistive Devices Robust to External Speakers
Our results show that this multi-task deep residual network is able to achieve a KWS accuracy relative improvement of around 32% with respect to a system that does not deal with external speakers.
