Voice control of dual-drive mobile robots-survey of algorithms

Sajkowski, M.

doi:10.1109/romoco.2002.1177137

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…However, because the control words may be embedded in speech, it is of paramount importance to implement safety mechanisms during the control. While there are several methods of speech recognition such as the Hidden Markov Models (HMMs) [9], Multiple Vector Quantization of HMMs (MVQHMMs) [8] or complex neural network-based voice recognition [3], and many prototypes of VCWs have been suggested during the past decade [7], none seems simple enough and robust to be implemented and developed for disabled individuals [6], [10].…”

Section: Introductionmentioning

confidence: 99%

Voice Recognition Algorithm for Portable Assistive Devices

2007

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We present here the implementation of a robust voice recognition algorithm for voice activated control of assistive devices. We implemented an effective method based on cross correlation of Mel Frequency Cepstral Coefficients (MFCC). The developed method yields high accuracy in low noise environment. Because our implementation is based on a set of training samples for each command, it can be easily adapted for any user. Once the training set is loaded, every command is compared to the MFCCs of all samples in the training set. We then use a "winner-takes-all" method to decide which group the command belongs to.

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Section: Introductionmentioning

confidence: 99%

Voice Recognition Algorithm for Portable Assistive Devices

2007

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“…We propose the original structure of the intelligent voice control system, and present experimental investigation of separate modules and outline the performance of the system by several simulation examples. Our approach differs from others [3] in twofold: the noise detection is carried out by specialized artificial neural network [4]; and the missing speech signal restoration is performed by using an intelligent multirate-processing scheme [5]. On the other hand, the recognition is done by using conventional Hidden Markov Models (HMM) approach.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Robot in an Adverse Environment: Intelligent Control by Voice

Navakauskas

Paulikas

2006

SSP

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In this paper we investigate the voice control of an autonomous robot in the presence of impulsive noise. We propose an original structure of the intelligent voice control system, present experimental investigation of separate modules and outline the performance of the system by the simulation example. Our approach differs from others in twofold: the noise detection is carried out by specialized artificial neural network; and the restoration of the missing speech signal is performed by using an intelligent multirate-processing scheme. The simplicity of neural network’s employment and unnecessary a priori knowledge of noise characteristics are the main merits of this approach. The employment of nonlinear re-sampling improved the precision of speech signal restoration and consequently increased the overall recognition performance.

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