A new approach to design reliable real-time speech recognition systems

2002 ◽  
Author(s):  
F. Vargas ◽  
R.D. Fagundes ◽  
D. Barros
Keyword(s):  
Speech Recognition ◽  
Real Time ◽  
New Approach ◽  
Recognition Systems

scholarly journals Real-time speech recognition systems

1990 ◽  
Author(s):  
Hy Murveit ◽  
Mitch Weintraub
Keyword(s):  
Speech Recognition ◽  
Real Time ◽  
Recognition Systems

An ASIC-Based Artificial Neural Network Applied Real-time Speech Recognition SOPC

2016 ◽  
Vol 2 (1) ◽  
pp. 38
Author(s):  
Lam D. Pham ◽  
Hieu M. Nguyen ◽  
Du N. N. T. Nguyen ◽  
Trang Hoang
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Speech Recognition ◽  
Real Time ◽  
Silicon Area ◽  
Pattern Recognition Systems ◽  
Recognition Systems ◽  
Real Time Applications ◽  
Artificial Neural

Artificial Neural Network (ANN) is promoted to one of major schemes applied in pattern recognition area. Indeed, many approaches to software-based platforms have proven great performance of ANN. However, developing pattern recognition systems integrating ANN hardware-based architecture has been limited not only by the silicon requirements such as frequency, area, power, or resource but also by high accuracy and real-time applications strictly. Although a considerable number of ANN hardware-based architectures have been proposed currently, they have experienced a deprivation of functions due to both small configurations and ability of reconfiguration. Consequently, achieving an effective ANN hardware-based architecture so as to adapt to not only strict accuracy, enormous configures, or silicon area but also real-time criterion in pattern recognition systems has been really challenged. To tackle these issues, this work has proposed a dynamic structure of three-layer ANN architecture being able to reconfigure for adapting to various real-time applications. What is more, a complete SOPC system integrating proposed ANN hardware has also implemented to apply Vietnamese speech recognition automatically to confirm high recognition probability around 95.2 % towards 20 Vietnamese discrete words. Moreover, experiment results on such ASIC-based architecture have witnessed maximum frequency at 250 MHz on 130nm technology as well as great ability of reconfiguration.

Acceleration Strategies for Speech Recognition Based on Deep Neural Networks

2014 ◽  
Vol 556-562 ◽  
pp. 5181-5185
Author(s):  
Chao Tian ◽  
Jia Liu ◽  
Zhao Meng Peng
Keyword(s):  
Speech Recognition ◽  
Real Time ◽  
Deep Neural Networks ◽  
Gaussian Mixture ◽  
Relative Reduction ◽  
Time Rate ◽  
Word Error Rate ◽  
Baseline System ◽  
Recognition Systems ◽  
Better Than

The Context-Dependent Deep-Neural-Network HMM, or CD-DNN-HMM, is a powerful acoustic modeling technique for HMM-based speech recognition systems. The CD-DNN-HMM can greatly outperform against the conventional Gaussian-mixture HMMs. Therefore, we build a CD-DNN-HMM LVCSR system by modifying a mature GMM-HMM system. The baseline CD-DNN-HMM system achieve word-error rate of 18.6% that is far better than 24.9% achieved by the GMM-HMM system. However, the speed of the baseline CD-DNN-HMM system becomes a major roadblock for its real-time rate reaches 0.72 on the standard NIST 2000 Hub5 evaluation set. In this paper, we realize several optimization algorithms in our baseline system to accelerate the recognition speed. Testing the optimized system on the same evaluation set, we achieve real-time rate of 0.39, a relative reduction of 45.8%.

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