Optimization of dictionary and model library for recognition of speech commands based on cross-correlation portraits

Krasheninnikov, V. R.; Krasheninnikova, Natalia A.; Kuznetsov, V. V.; Lebedeva, E. Yu.

doi:10.1134/s1054661813010070

Cited by 3 publications

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“…In noisy environments, voice recognition accuracy can be enhanced by special digital signal processing systems. In this paper, we propose methods for describing the voice messages of some typical phrases using mathematical models of random processes [2][3][4], which can also be used in differern processing algorithms.…”

Section: Introductionmentioning

confidence: 99%

Correlation analysis of voice communications in Russian language in the airspace of the Russian Federation

Andriyanov¹,

Buldakovsky²,

Risukhin³

2019

ITM Web Conf.

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The spectral characteristics of audio recordings of typical phrases of radio exchange of air traffic controllers and civil aviation pilots are investigated. Based on the obtained spectral characteristics, experimental covariance functions are constructed that show statistical relationships within a single voice message in the time domain. The main attention is paid to the possibility of describing speech messages using mathematical models of random sequences.

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Section: Introductionmentioning

confidence: 99%

Correlation analysis of voice communications in Russian language in the airspace of the Russian Federation

Andriyanov¹,

Buldakovsky²,

Risukhin³

2019

ITM Web Conf.

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“…For this purpose, it is necessary for pronunciation variability to be somehow reflected in portraits. Let us consider a cross-correlation portrait (CCP), which consists of correlation coefficients between segments of two VCs (cross-correlation) [15,16,17,22]. Let there be two VCs S 1 and S 2.…”

Section: Cross-correlation Portraitsmentioning

confidence: 99%

Voice command recognition for noisy environments by means of cross-correlation portraits

Armer¹,

Galitskaya²,

Krasheninnikova³

2017

Collection of Selected Papers of the III International Conference on Information Technology and Nanotechnology

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Methods of voice command (VC) recognition in heavy noise environments are required for precise work of speech information systems on the factory floor and in transport. The paper considers a speaker-dependent way of VC recognition for VCs belonging to a limited vocabulary and being recognized in heavy noise environments. For this purpose, VCs are transformed into cross-correlation portraits (CCPs), i.e. special images. The VC under recognition is referred to a class with a minimal distance (metric) between CCP of this command and model CCPs of the class. The authors elaborated algorithms for VC transformation into CCPs, a method for defining VC boundaries, ways of model command optimization and metric choice. As a result, a rather precise VC recognition in heavy noise environment was obtained.

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Multidimensional Image Models and Processing

Krasheninnikov

Васильев

2017

Intelligent Systems Reference Library

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Optimization of dictionary and model library for recognition of speech commands based on cross-correlation portraits

Cited by 3 publications

References 0 publications

Correlation analysis of voice communications in Russian language in the airspace of the Russian Federation

Correlation analysis of voice communications in Russian language in the airspace of the Russian Federation

Voice command recognition for noisy environments by means of cross-correlation portraits

Multidimensional Image Models and Processing

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