Research on a software architecture of speech recognition and detection based on interactive reconstruction model
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Research on a software architecture of speech recognition and detection based on interactive reconstruction model Xianzhen Ren1 Received: 13 January 2020 / Accepted: 29 October 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract The natural, fast, stable and reliable interaction between human and machine is the ideal interaction mode pursued by human beings. Speech recognition is a process of pattern matching recognition. Effective speech detection technology can not only reduce the processing time of the system, improve the real-time and accuracy of the system processing, but also eliminate the noise interference of the silent segment, so as to improve the subsequent recognition performance. Through speech recognition, the machine can understand human language, complete the corresponding calculation tasks according to these instructions, and meet the needs of people. The traditional speech recognition method uses acoustic features to describe the model. No matter using model compensation method or normalization method, it can not solve the influence of speaker difference on the performance of recognition system. In this paper, we study a speech recognition detection system based on almost interactive reconstruction model, and analyze the continuous speech signal. The experimental results show that the proposed method has a high recognition rate and a shorter time. Compared with the state-of-the-art methodologies, the proposed model can achieve the better performance. Keywords Speech recognition · Reconstruction model · Interactive design · Endpoint detection · System framework · Data sampling
1 Introduction Speech is the main medium for many organisms to transmit information in nature. Speech information carries the most direct, clear and clear information. With the development of computer technology, pattern recognition and signal processing technology, speech recognition system has been improved and optimized, which makes it widely used in home appliances, communication, automotive electronics, medical, consumer electronics and other fields. In the embedded field, speech recognition technology is also being widely studied and applied. Especially since entering the twenty-first century, the emergence and application of speech recognition system represented by mobile terminals has set off a wave of research on speech recognition technology again (Martin and Jurafsky 2000; Müller 2007; Lu and Wu 2011; Wu and Fan 2005). At present, the speech * Xianzhen Ren [email protected] 1
Beijing Information Technology College, Beijing 100018, China
recognition system has a high recognition performance in the ideal laboratory environment, but there are various kinds of noise interference in the actual environment, which will lead to the mismatch between the test environment and the training environment, so that the performance of the speech recognition system drops sharply. In the construction of speech recognition model, the research focus is to design a speech recognition system with
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