An overview of noise-robust automatic speech recognition
New waves of consumer-centric applications, such as voice search and voice interaction
with mobile devices and home entertainment systems, increasingly require automatic …
with mobile devices and home entertainment systems, increasingly require automatic …
Robust automatic speech recognition: a bridge to practical applications
Robust Automatic Speech Recognition: A Bridge to Practical Applications establishes a solid
foundation for automatic speech recognition that is robust against acoustic environmental …
foundation for automatic speech recognition that is robust against acoustic environmental …
[PDF][PDF] Generation of Large-Scale Simulated Utterances in Virtual Rooms to Train Deep-Neural Networks for Far-Field Speech Recognition in Google Home.
We describe the structure and application of an acoustic room simulator to generate large-
scale simulated data for training deep neural networks for far-field speech recognition. The …
scale simulated data for training deep neural networks for far-field speech recognition. The …
Power-normalized cepstral coefficients (PNCC) for robust speech recognition
This paper presents a new feature extraction algorithm called power normalized Cepstral
coefficients (PNCC) that is motivated by auditory processing. Major new features of PNCC …
coefficients (PNCC) that is motivated by auditory processing. Major new features of PNCC …
Delta-spectral cepstral coefficients for robust speech recognition
Almost all current automatic speech recognition (ASR) systems conventionally append delta
and double-delta cepstral features to static cepstral features. In this work we describe a …
and double-delta cepstral features to static cepstral features. In this work we describe a …
[PDF][PDF] Robust CNN-based speech recognition with Gabor filter kernels.
As has been extensively shown, acoustic features for speech recognition can be learned
from neural networks with multiple hidden layers. However, the learned transformations may …
from neural networks with multiple hidden layers. However, the learned transformations may …
Normalized amplitude modulation features for large vocabulary noise-robust speech recognition
Background noise and channel degradations seriously constrain the performance of state-of-
the-art speech recognition systems. Studies comparing human speech recognition …
the-art speech recognition systems. Studies comparing human speech recognition …
Improving the Mann–Whitney statistical test for feature selection: An approach in breast cancer diagnosis on mammography
Objective This work addresses the theoretical description and experimental evaluation of a
new feature selection method (named uFilter). The uFilter improves the Mann–Whitney U …
new feature selection method (named uFilter). The uFilter improves the Mann–Whitney U …
Improved wireless acoustic sensor network for analysing audio properties
In this study, a Wireless Acoustic Sensor Network (WASN) technique has been designed for
evaluating various features of audio signals and assessing different audio qualities in a …
evaluating various features of audio signals and assessing different audio qualities in a …
Deep-sparse-representation-based features for speech recognition
Features derived using sparse representation (SR)-based approaches have been shown to
yield promising results for speech recognition tasks. In most of the approaches, the SR …
yield promising results for speech recognition tasks. In most of the approaches, the SR …