A VLSI dynamic time warp processor for connected and isolated word speech recognition

Isolated-word speech-recognition system adopted the shortest distance of Dynamic Time Warping (DTW) to make recognition judgment, which has the disadvantage of high False Accept Rate (FAR), poor anti-noise and robustness. This paper proposes a new method based on DTW distance Threshold Estimation for recognition judgment. This method processes the maximum distance between template speech and training input speech multiplying adjusting coefficient, then plus noise DTW distance, which regard the final result as distance Threshold Estimation. At the time of doing speech recognition, if the distance between testing speech and template speech exceeds the Threshold Estimation, then the system will not recognize this speech. The experiment shows that this method can greatly improve the anti-noise and robustness performance of the Isolated-word speech-recognition system and solve the problem of high FAR.

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Chinese Connected Word Speech Recognition Based on Derivative Dynamic Time Warping

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.542-543.1324 ◽

2012 ◽

Vol 542-543 ◽

pp. 1324-1329

Author(s):

Zhi Guo He ◽

Ze Min Liu

Keyword(s):

Speech Recognition ◽

Computational Complexity ◽

Dynamic Time Warping ◽

Recognition Rate ◽

Experimental Results ◽

Time Warping ◽

Test String ◽

Cumulative Distance ◽

Isolated Word ◽

Dynamic Time

The algorithm of derivative dynamic time warping (DDTW) can overcome the shortcoming of dynamic time warping (DTW) and the computational complexity has not increased. In this paper, the algorithm of DDTW was applied to Chinese connected word speech recognition. For each isolated word, as an independent reference template and as basic recognition unit, there was an independent reference template to correspond; the matching between some word of the test string and a reference template was done by the DDTW, and the reference string which had the minimum cumulative distance was as output. The experimental results show that our method is obviously superior to all the methods based on DTW, and the recognition rate has reached 90%.

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Development of HMM/Neural Network‐Based Medium‐Vocabulary Isolated‐Word Lithuanian Speech Recognition System

Informatica ◽

10.15388/informatica.2004.073 ◽

2004 ◽

Vol 15 (4) ◽

pp. 465-474 ◽

Cited By ~ 1

Author(s):

Mark Filipovič ◽

Antanas Lipeika

Keyword(s):

Neural Network ◽

Speech Recognition ◽

Recognition System ◽

Speech Recognition System ◽

Isolated Word

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Environmental effects on reliability and accuracy of MFCC based voice recognition for industrial human-robot-interaction

Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture ◽

10.1177/09544054211014492 ◽

2021 ◽

pp. 095440542110144

Author(s):

B Birch ◽

CA Griffiths ◽

A Morgan

Keyword(s):

Speech Recognition ◽

Voice Recognition ◽

Human Robot Interaction ◽

Hole Drilling ◽

Time Warping ◽

Mel Frequency Cepstral Coefficients ◽

Robot Interaction ◽

Extraction Algorithm ◽

Dynamic Time ◽

Manufacturing Environments

Collaborative robots are becoming increasingly important for advanced manufacturing processes. The purpose of this paper is to determine the capability of a novel Human-Robot-interface to be used for machine hole drilling. Using a developed voice activation system, environmental factors on speech recognition accuracy are considered. The research investigates the accuracy of a Mel Frequency Cepstral Coefficients-based feature extraction algorithm which uses Dynamic Time Warping to compare an utterance to a limited, user-dependent dictionary. The developed Speech Recognition method allows for Human-Robot-Interaction using a novel integration method between the voice recognition and robot. The system can be utilised in many manufacturing environments where robot motions can be coupled to voice inputs rather than using time consuming physical interfaces. However, there are limitations to uptake in industries where the volume of background machine noise is high.

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