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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235 Citations
Highly Influential Citations
34
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73
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87
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235 Citations

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Citation Type

Robust speech features and acoustic models for speech recognition
TLDR
This thesis examines techniques to improve the robustness of automatic speech recognition (ASR) systems against noise distortions, and proposes to normalize the temporal structure of both training and testing speech features to reduce the feature-model mismatch. Expand
Microphone array processing for robust speech recognition
TLDR
A new approach to microphone-array processing is proposed in which the goal of the array processing is not to generate an enhanced output waveform but rather to generate a sequence of features which maximizes the likelihood of the correct hypothesis. Expand
HMM Adaptation Using Statistical Linear Approximation for Robust Speech Recognition
TLDR
The proposed robustness method is highly attractive for the Distributed Speech Recognition (DSR) architecture, since there is no impact on the Front End structure and neither on the ASR topology. Expand
Missing-Feature Approaches in Speech Recognition [ Improving recognition accuracy in noise by using partial spectrographic information ]
D espite decades of focused research on the problem, the accuracy of automatic speech recognition (ASR) systems is still adversely affected by noise and other sources of acoustical variability. For… Expand
Likelihood-Maximizing-Based Multiband Spectral Subtraction for Robust Speech Recognition
TLDR
A novel approach for solving the problem of automatic speech recognition performance degradation by considering SS and the speech recognizer not as two independent entities cascaded together, but rather as two interconnected components of a single system, sharing the common goal of improved speech recognition accuracy. Expand
Likelihood-Maximizing-BasedMultiband Spectral Subtraction for Robust Speech Recognition
Automatic speech recognition performance degrades significantly when speech is affected by environmental noise. Nowadays, the major challenge is to achieve good robustness in adverse noisy conditions… Expand
Cepstral compensation by polynomial approximation for environment-independent speech recognition
TLDR
This work introduces an approximation based method to compute the effects of the environment on the parameters of the PDF of clean speech, and performs compensation by vector polynomial approximations (VPS) for the effect of linear filtering and additive noise on the clean speech. Expand
Model-space compensation of microphone and noise for speaker-independent speech recognition
  • Y. Gong
  • Computer Science
  • 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03).
  • 2003
TLDR
A method, called JAC (Joint compensation of Additive and Convolutive distortions), is presented that uses two log-spectral domain components in speech acoustic models to represent additive and convolutive distortions and reduces recognition word error rate by an order of magnitude. Expand
Multi-channel Feature Enhancement for Robust Speech Recognition
TLDR
In the last decades, a great deal of research has been devoted to extending the authors' capacity of verbal communication with computers through automatic speech recognition, but there are still obstacles to overcome in making reliable distant speech recognition (DSR) systems. Expand
Enhancement of adaptive de-correlation filtering separation model for robust speech recognition
  • R. Hu
  • Computer Science
  • 2007
TLDR
Improvements in speech spectral characteristics, prewhitening procedures are applied to flatten the long-term speech spectrum to improve adaptation robustness and decrease ADF estimation error, and block-iterative implementation and variable step-size methods are proposed for speedup convergence rate. Expand
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References

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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
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