Speech Recognition Based on Pattern Recognition Approaches

1992 ◽  
pp. 111-126 ◽  
Author(s):  
Lawrence R. Rabiner
Keyword(s):  
Pattern Recognition ◽  
Speech Recognition

ON THE USE OF THE MORPHIC GENERATOR GRAMMATICAL INFERENCE (MGGI) METHODOLOGY IN AUTOMATIC SPEECH RECOGNITION

1990 ◽  
Vol 04 (04) ◽  
pp. 667-685 ◽  
Author(s):  
PEDRO GARCÍA ◽  
ENCARNA SEGARRA ◽  
ENRIQUE VIDAL ◽  
ISABEL GALIANO
Keyword(s):  
Pattern Recognition ◽  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Regular Languages ◽  
Grammatical Inference ◽  
Real Problem ◽  
Syntactic Pattern Recognition ◽  
General Methodology ◽  
Syntactic Pattern

Recently, a new methodology, referred to as “Morphic Generator Grammatical Inference” (MGGI), has been introduced as a step towards a general methodology for the inference of regular languages. In this paper we consider the application of this methodology to a real problem of automatic speech recognition, thus allowing (and also requiring) the proposed problem to be properly formulated within the canonical framework of syntactic pattern recognition. The results show both the viability and appropriateness of the application of MGGI to the problem considered.

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.

A Life-Based Classifier for Automatic Speech Recognition

2014 ◽  
Vol 679 ◽  
pp. 189-193 ◽  
Author(s):  
Rosemary T. Salaja ◽  
Ronan Flynn ◽  
Michael Russell
Keyword(s):  
Pattern Recognition ◽  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Artificial Life ◽  
Recognition System ◽  
Recognition Problem ◽  
Speech Recognition System ◽  
Pattern Recognition Problem ◽  
Asr System

Research in speech recognition has produced different approaches that have been used for the classification of speech utterances in the back-end of an automatic speech recognition (ASR) system. As speech recognition is a pattern recognition problem, classification is an important part of any speech recognition system. This paper proposes a new back-end classifier that is based on artificial life (ALife) and describes how the proposed classifier can be used in a speech recognition system.

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