Missing data techniques for robust speech recognition

Cooke, Martin; Morris, Andrew; Green, P.

doi:10.1109/icassp.1997.596072

Cited by 62 publications

(29 citation statements)

References 6 publications

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“…The missing data approach assumes that some acoustic data in the mixture will remain uncorrupted and can be identified as reliable evidence for recognition. Cooke et al (1997) demonstrated that recognition can indeed be based on a small amount (10% or less) of the original time-frequency 'pixels' if they can be correctly identified.…”

Section: Linking Casa With Speech Recognition Systemsmentioning

confidence: 99%

Exploiting correlogram structure for robust speech recognition with multiple speech sources

Green

Barker

et al. 2007

Speech Communication

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Section: Linking Casa With Speech Recognition Systemsmentioning

confidence: 99%

Exploiting correlogram structure for robust speech recognition with multiple speech sources

Green

Barker

et al. 2007

Speech Communication

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“…The first approach is known as the marginalisation approach and, in brief, it basically involves modifying the computation of the observation probabilities in the recogniser to take into account the missing information [7,8]. The second approach, known as imputation, involves "filling in" the missing information in the noisy spectrum before speech recognition actually happens [16,20,36,37,42].…”

Section: Comparison With Other Missing-data Techniquesmentioning

confidence: 99%

Spectral Reconstruction and Noise Model Estimation Based on a Masking Model for Noise Robust Speech Recognition

González

Gómez²,

Peinado³

et al. 2017

Circuits Syst Signal Process

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An effective way to increase noise robustness in automatic speech recognition (ASR) systems is feature enhancement based on an analytical distortion model that describes the effects of noise on the speech features. One of such distortion models that has been reported to achieve a good tradeoff between accuracy and simplicity is the masking model. Under this model, speech distortion caused by environmental noise is seen as a spectral mask and, as a result, noisy speech features can be either reliable (speech is not masked by noise) or unreliable (speech is masked). In this paper we present a detailed overview of this model and its applications to noise-robust ASR. Firstly, using the masking model, we derive a spectral reconstruction technique aimed at enhancing the noisy speech features. Two problems must be solved in order to perform spectral reconstruction using the masking model: i) mask estimation, i.e. determining the reliability of the noisy features, and ii) feature imputation, i.e. estimating speech for the unreliable features. Unlike missing-data imputation techniques where the two problems are considered as independent, our technique jointly addresses them by exploiting a priori knowledge of the speech and noise sources in the form of a statistical model. Secondly, we propose an algorithm for estimating the noise model required by the feature enhancement technique. The proposed algorithm fits a Gaussian mixture model (GMM) to the noise by iteratively maximising the likelihood of the noisy speech signal so that noise can be estimated even during speech-dominating frames. A comprehensive set of experiments carried out on the Aurora-2 and Aurora-4 databases shows that the proposed method achieves significant improvements over the baseline system and other similar missing-data imputation techniques.

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“…We next turn to missing feature techniques, which can be used to model feature distortion due to a front-end enhancement process [7], noise [49], or reverberation [50].…”

Section: Missing Feature Techniquesmentioning

confidence: 99%

A Bayesian view on acoustic model-based techniques for robust speech recognition

Maas

Huemmer

Sehr

et al. 2015

EURASIP J. Adv. Signal Process.

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This article provides a unifying Bayesian view on various approaches for acoustic model adaptation, missing feature, and uncertainty decoding that are well-known in the literature of robust automatic speech recognition. The representatives of these classes can often be deduced from a Bayesian network that extends the conventional hidden Markov models used in speech recognition. These extensions, in turn, can in many cases be motivated from an underlying observation model that relates clean and distorted feature vectors. By identifying and converting the observation models into a Bayesian network representation, we formulate the corresponding compensation rules. We thus summarize the various approaches as approximations or modifications of the same Bayesian decoding rule leading to a unified view on known derivations as well as to new formulations for certain approaches.

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Missing data techniques for robust speech recognition

Cited by 62 publications

References 6 publications

Exploiting correlogram structure for robust speech recognition with multiple speech sources

Exploiting correlogram structure for robust speech recognition with multiple speech sources

Spectral Reconstruction and Noise Model Estimation Based on a Masking Model for Noise Robust Speech Recognition

A Bayesian view on acoustic model-based techniques for robust speech recognition

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