Corpus ID: 108746063

Speech recognition in noisy environments

@inproceedings{Moreno1996SpeechRI,
  title={Speech recognition in noisy environments},
  author={Pedro J. Moreno},
  year={1996}
}
The accuracy of speech recognition systems degrades severely when the systems are operated in adverse acoustical environments. In recent years many approaches have been developed to address the problem of robust speech recognition, using feature-normalization algorithms, microphone arrays, representations based on human hearing, and other approaches. Nevertheless, to date the improvement in recognition accuracy afforded by such algorithms has been limited, in part because of inadequacies in… Expand
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References

SHOWING 1-10 OF 52 REFERENCES
Acoustical and environmental robustness in automatic speech recognition
TLDR
This dissertation describes a number of algorithms developed to increase the robustness of automatic speech recognition systems with respect to changes in the environment, including the SNR-Dependent Cepstral Normalization, (SDCN) and the Codeword-Dependent Cep stral normalization (CDCN). Expand
A unified approach for robust speech recognition
TLDR
Three techniques that share the same basic assumptions and internal structure but differ in whether they modify the incoming speech cepstra or whether they modifying the classifier statistics are presented, which is to unify these approaches to robust speech recognition. Expand
APPROACHES TO ENVIRONMENT COMPENSATION IN AUTOMATIC SPEECH RECOGNITION
This paper describes a series of cepstral-based compensation procedures that render the SPHINX-II continuous speech recognition system more robust with respect to acoustical changes in theExpand
Efficient joint compensation of speech for the effects of additive noise and linear filtering
  • Fu-hua Liu, A. Acero, R. Stern
  • Computer Science
  • [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing
  • 1992
TLDR
Two algorithms are described that provide robustness for automatic speech recognition systems in a fashion that is suitable for real-time environmental normalization for workstations of moderate size and a modification of the more complex CDCN algorithm that enables it to perform environmental compensation in better than real time. Expand
Model-based techniques for noise robust speech recognition
TLDR
The development of a model-based noise compensation technique, Parallel Model Combination, to alter the parameters of a set of Hidden Markov Model (HMM) based acoustic models, so that they reeect speech spoken in a new acoustic environment is detailed. Expand
Adaptation to New Microphones Using Tied-Mixture Normalization
TLDR
Experimental results show that the proposed algorithm, combined with cepstrum mean subtraction, improves the recognition accuracy when the system is tested on a microphone with different characteristics than the one on which it was trained. Expand
Rapid environment adaptation for robust speech recognition
TLDR
A rapid environment adaptation algorithm based on spectrum equalization (REALISE) improved recognition accuracy from 87% to 96% in a 250 Japanese word recognition task. Expand
The HTK large vocabulary recognition system for the 1995 ARPA H3 task
TLDR
Developments of the HTK large vocabulary speech recognition system aimed at recognition of speech from the ARPA H3 task which contains data of a relatively low signal-to-noise ratio from unknown microphones are described. Expand
Probabilistic optimum filtering for robust speech recognition
  • L. Neumeyer, M. Weintraub
  • Computer Science
  • Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing
  • 1994
TLDR
A new mapping algorithm for speech recognition that relates the features of simultaneous recordings of clean and noisy speech to reduce recognition errors when the training and testing acoustic environments do not match is presented. Expand
Signal Processing for Robust Speech Recognition
TLDR
Use of the various compensation algorithms in consort produces a reduction of error rates for SPHINX-II by as much as 40 percent relative to the rate achieved with cepstral mean normalization alone, in both development test sets and in the context of the 1993 ARPA CSR evaluations. Expand
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