The application of hidden Markov models in speech recognition
The Application of Hidden Markov Models in Speech Recognition Page 1 The Application of
Hidden Markov Models in Speech Recognition Full text available at: http://dx.doi.org/10.1561/2000000004 …
Hidden Markov Models in Speech Recognition Full text available at: http://dx.doi.org/10.1561/2000000004 …
Developments of Machine Learning Schemes for Dynamic Time‐Wrapping‐Based Speech Recognition
IJ Ding, CT Yen, YM Hsu - Mathematical Problems in …, 2013 - Wiley Online Library
This paper presents a machine learning scheme for dynamic time‐wrapping‐based (DTW)
speech recognition. Two categories of learning strategies, supervised and unsupervised …
speech recognition. Two categories of learning strategies, supervised and unsupervised …
An ensemble speaker and speaking environment modeling approach to robust speech recognition
We propose an ensemble speaker and speaking environment modeling (ESSEM) approach
to characterizing environments in order to enhance performance robustness of automatic …
to characterizing environments in order to enhance performance robustness of automatic …
Noise in HMM-based speech synthesis adaptation: Analysis, evaluation methods and experiments
This work describes experiments on using noisy adaptation data to create personalized
voices with HMM-based speech synthesis. We investigate how environmental noise affects …
voices with HMM-based speech synthesis. We investigate how environmental noise affects …
Robust speech recognition under noisy ambient conditions
KK Paliwal, K Yao - Human-centric interfaces for ambient intelligence, 2010 - Elsevier
Publisher Summary This chapter provides an overview of an automatic speech recognition
system and describes sources of speech variability that cause mismatch between training …
system and describes sources of speech variability that cause mismatch between training …
Robust several-speaker speech recognition with highly dependable online speaker adaptation and identification
PY Shih, PC Lin, JF Wang, YN Lin - Journal of network and computer …, 2011 - Elsevier
The currently adaptive mechanisms adapt a single acoustic model for a speaker in speaker-
independent speech recognition system. However, as more users use the same speech …
independent speech recognition system. However, as more users use the same speech …
Factored MLLR adaptation
One of the most popular approaches to parameter adaptation in hidden Markov model
(HMM) based systems is the maximum likelihood linear regression (MLLR) technique. In this …
(HMM) based systems is the maximum likelihood linear regression (MLLR) technique. In this …
Maximum penalized likelihood kernel regression for fast adaptation
This paper proposes a nonlinear generalization of the popular maximum-likelihood linear
regression (MLLR) adaptation algorithm using kernel methods. The proposed method …
regression (MLLR) adaptation algorithm using kernel methods. The proposed method …
Rapid speaker adaptation using clustered maximum-likelihood linear basis with sparse training data
Speaker space-based adaptation methods for automatic speech recognition have been
shown to provide significant performance improvements for tasks where only a few seconds …
shown to provide significant performance improvements for tasks where only a few seconds …
[PDF][PDF] Factored MLLR adaptation for singing voice generation
In our previous study, we proposed factored MLLR (FMLLR) where each MLLR parameter is
defined as a function of a control vector. We presented a method to train the FMLLR …
defined as a function of a control vector. We presented a method to train the FMLLR …