Acoustic modeling problem for automatic speech recognition system: advances and refinements (Part II)

Authors:
Rajesh Kumar Aggarwal;M. Dave
Affiliations:
National Institute of Technology, Kurukshetra, India;National Institute of Technology, Kurukshetra, India
Venue:
International Journal of Speech Technology
Year:
2011

Citing 35
Cited 2

A time-delay neural network architecture for isolated word recognition

Neural Networks
Links Between Markov Models and Multilayer Perceptrons

IEEE Transactions on Pattern Analysis and Machine Intelligence
A generalized hidden Markov model with state-conditioned trend functions of time for the speech signal

Signal Processing
On Combining Classifiers

IEEE Transactions on Pattern Analysis and Machine Intelligence
Cepstral domain segmental feature vector normalization for noise robust speech recognition

Speech Communication - Special issue on robust speech recognition
Heteroscedastic discriminant analysis and reduced rank HMMs for improved speech recognition

Speech Communication
The Hierarchical Hidden Markov Model: Analysis and Applications

Machine Learning
Comparison of discriminative training criteria and optimization methods for speech recognition

Speech Communication
Connectionist Speech Recognition: A Hybrid Approach

Connectionist Speech Recognition: A Hybrid Approach
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
Experiments in Speaker Normalisation and Adaptation for Large Vocabulary Speech Recognition

ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Pattern Recognition and Machine Learning (Information Science and Statistics)

Pattern Recognition and Machine Learning (Information Science and Statistics)
A vector Taylor series approach for environment-independent speech recognition

ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 02
Temporal patterns (TRAPs) in ASR of noisy speech

ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01
Buried Markov models for speech recognition

ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
A new look at discriminative training for hidden Markov models

Pattern Recognition Letters
Using multiple acoustic feature sets for speech recognition

Speech Communication
Dynamic Bayesian networks for audio-visual speech recognition

EURASIP Journal on Applied Signal Processing
A new approach for the adaptation of HMMs to reverberation and background noise

Speech Communication
The application of hidden Markov models in speech recognition

Foundations and Trends in Signal Processing
Semi-continuous HMMs with explicit state duration for unconstrained Arabic word modeling and recognition

Pattern Recognition Letters
Efficient likelihood evaluation and dynamic Gaussian selection for HMM-based speech recognition

Computer Speech and Language
Continuously variable duration hidden Markov models for automatic speech recognition

Computer Speech and Language
Discriminative training of HMMs for automatic speech recognition: A survey

Computer Speech and Language
A study on the generalization capability of acoustic models for robust speech recognition

IEEE Transactions on Audio, Speech, and Language Processing
Dual stream speech recognition using articulatory syllable models

International Journal of Speech Technology
Linear discriminant analysis for improved large vocabulary continuous speech recognition

ICASSP'92 Proceedings of the 1992 IEEE international conference on Acoustics, speech and signal processing - Volume 1
Tandem connectionist feature extraction for conversational speech recognition

MLMI'04 Proceedings of the First international conference on Machine Learning for Multimodal Interaction
Combining Spectral Representations for Large-Vocabulary Continuous Speech Recognition

IEEE Transactions on Audio, Speech, and Language Processing
New insights into the noise reduction Wiener filter

IEEE Transactions on Audio, Speech, and Language Processing
Minimum phone error training of precision matrix models

IEEE Transactions on Audio, Speech, and Language Processing
Template-Based Continuous Speech Recognition

IEEE Transactions on Audio, Speech, and Language Processing
Large margin hidden Markov models for speech recognition

IEEE Transactions on Audio, Speech, and Language Processing
Large-Margin Estimation of Hidden Markov Models With Second-Order Cone Programming for Speech Recognition

IEEE Transactions on Audio, Speech, and Language Processing

Filterbank optimization for robust ASR using GA and PSO

International Journal of Speech Technology
Integration of multiple acoustic and language models for improved Hindi speech recognition system

International Journal of Speech Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

In automatic speech recognition (ASR) systems, hidden Markov models (HMMs) have been widely used for modeling the temporal speech signal. As discussed in Part I, the conventional acoustic models used for ASR have many drawbacks like weak duration modeling and poor discrimination. This paper (Part II) presents a review on the techniques which have been proposed in literature for the refinements of standard HMM methods to cope with their limitations. Current advancements related to this topic are also outlined. The approaches emphasized in this part of review are connectionist approach, explicit duration modeling, discriminative training and margin based estimation methods. Further, various challenges and performance issues such as environmental variability, tied mixture modeling, and handling of distant speech signals are analyzed along with the directions for future research.