680 research outputs found
Transfer Learning for Speech and Language Processing
Transfer learning is a vital technique that generalizes models trained for
one setting or task to other settings or tasks. For example in speech
recognition, an acoustic model trained for one language can be used to
recognize speech in another language, with little or no re-training data.
Transfer learning is closely related to multi-task learning (cross-lingual vs.
multilingual), and is traditionally studied in the name of `model adaptation'.
Recent advance in deep learning shows that transfer learning becomes much
easier and more effective with high-level abstract features learned by deep
models, and the `transfer' can be conducted not only between data distributions
and data types, but also between model structures (e.g., shallow nets and deep
nets) or even model types (e.g., Bayesian models and neural models). This
review paper summarizes some recent prominent research towards this direction,
particularly for speech and language processing. We also report some results
from our group and highlight the potential of this very interesting research
field.Comment: 13 pages, APSIPA 201
Adaptation Algorithms for Neural Network-Based Speech Recognition: An Overview
We present a structured overview of adaptation algorithms for neural
network-based speech recognition, considering both hybrid hidden Markov model /
neural network systems and end-to-end neural network systems, with a focus on
speaker adaptation, domain adaptation, and accent adaptation. The overview
characterizes adaptation algorithms as based on embeddings, model parameter
adaptation, or data augmentation. We present a meta-analysis of the performance
of speech recognition adaptation algorithms, based on relative error rate
reductions as reported in the literature.Comment: Submitted to IEEE Open Journal of Signal Processing. 30 pages, 27
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Automatic speech recognition: from study to practice
Today, automatic speech recognition (ASR) is widely used for different purposes such as robotics, multimedia, medical and industrial application. Although many researches have been performed in this field in the past decades, there is still a lot of room to work. In order to start working in this area, complete knowledge of ASR systems as well as their weak points and problems is inevitable. Besides that, practical experience improves the theoretical knowledge understanding in a reliable way. Regarding to these facts, in this master thesis, we have first reviewed the principal structure of the standard HMM-based ASR systems from technical point of view. This includes, feature extraction, acoustic modeling, language modeling and decoding. Then, the most significant challenging points in ASR systems is discussed. These challenging points address different internal components characteristics or external agents which affect the ASR systems performance. Furthermore, we have implemented a Spanish language recognizer using HTK toolkit. Finally, two open research lines according to the studies of different sources in the field of ASR has been suggested for future work
Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments
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
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