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 …
Machine learning paradigms for speech recognition: An overview
L Deng, X Li - IEEE Transactions on Audio, Speech, and …, 2013 - ieeexplore.ieee.org
Automatic Speech Recognition (ASR) has historically been a driving force behind many
machine learning (ML) techniques, including the ubiquitously used hidden Markov model …
machine learning (ML) techniques, including the ubiquitously used hidden Markov model …
Deep learning: methods and applications
This monograph provides an overview of general deep learning methodology and its
applications to a variety of signal and information processing tasks. The application areas …
applications to a variety of signal and information processing tasks. The application areas …
Noisy training for deep neural networks in speech recognition
Deep neural networks (DNNs) have gained remarkable success in speech recognition,
partially attributed to the flexibility of DNN models in learning complex patterns of speech …
partially attributed to the flexibility of DNN models in learning complex patterns of speech …
Acoustic modeling with hierarchical reservoirs
F Triefenbach, A Jalalvand… - … on Audio, Speech …, 2013 - ieeexplore.ieee.org
Accurate acoustic modeling is an essential requirement of a state-of-the-art continuous
speech recognizer. The Acoustic Model (AM) describes the relation between the observed …
speech recognizer. The Acoustic Model (AM) describes the relation between the observed …
Deep neural network acoustic models for spoken assessment applications
In this paper, we investigate the effectiveness of applying deep neural network hidden
Markov models, or DNN-HMMs, for acoustic modeling in the context of educational …
Markov models, or DNN-HMMs, for acoustic modeling in the context of educational …
Sequence classification using the high-level features extracted from deep neural networks
The recent success of deep neural networks (DNNs) in speech recognition can be attributed
largely to their ability to extract a specific form of high-level features from raw acoustic data …
largely to their ability to extract a specific form of high-level features from raw acoustic data …
An auditory inspired amplitude modulation filter bank for robust feature extraction in automatic speech recognition
N Moritz, J Anemüller… - IEEE/ACM Transactions on …, 2015 - ieeexplore.ieee.org
The human ability to classify acoustic sounds is still unmatched compared to recent methods
in machine learning. Psychoacoustic and physiological studies indicate that the auditory …
in machine learning. Psychoacoustic and physiological studies indicate that the auditory …
Foundations and trends in signal processing: Deep learning–methods and applications
This monograph provides an overview of general deep learning methodology and its
applications to a variety of signal and information processing tasks. The application areas …
applications to a variety of signal and information processing tasks. The application areas …
Classifier architectures for acoustic scenes and events: implications for DNNs, TDNNs, and perceptual features from DCASE 2016
J Schröder, N Moritz, J Anemüller… - … on Audio, Speech …, 2017 - ieeexplore.ieee.org
This paper evaluates neural network (NN) based systems and compares them to Gaussian
mixture model (GMM) and hidden Markov model (HMM) approaches for acoustic scene …
mixture model (GMM) and hidden Markov model (HMM) approaches for acoustic scene …