The effectiveness of the implementation of speech command recognition algorithms in embedded systems



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The effectiveness of the implementation of speech command recognition algorithms in embedded systems

Shukurov K.E 1, Turaev B.Sh.1, Khasanov U1


1 TUIT named after Mukhammad al-Khwarazmi, Artificial intelligence department, Uzbekistan
nobleman041197@gmail.com, keshukurov@gmail.com,ranoanarkulova92@gmail.com



AbstractSpeech is the basis of human-computer interfaces, smart house, IoT and control systems. Implementing a real time voice control system through speech commands recognition in different environments requires simple algorithms and special purpose systems. The article analyzes the existing speech commands recognition algorithms in embedded systems. A simple and efficient algorithm for voice control systems through limited speech commands has been proposed.
Keywords Speech commands recognition; human-computer interface (HCI); hardware-software platform; embedded system; Short time Fourier transform (STFT); Singular value decomposition (SVD); Mel Frequency Cepstral Coefficients (MFCC); Vector quantization (VQ); Dynamic time warping (DTW); Hidden Markov model.

  1. Introduction


Speech command control is widely used in various fields, speech control of devices and equipment, voice assistant in call centers, smart home and Internet of Things (IoT), speech identification, speech control interface for people with disabilities and can be applied other fields [1]. In this case, the processing of speech signals involves complex recognition algorithms.
Implementation of such human-computer interfaces requires special hardware and software platforms. However, with the increasing complexity of speech processing algorithms and the number of speech commands, the computing resource and memory size of the hardware-software platform increase dramatically. In addition, most human-computer interfaces work in real time. When recognizing speech commands in different environments, speech signals are affected by external noise and interference. Real-time mode, in turn, requires speed, compactness, and ease of use [2, 3, 4, 5, 6, 7, 8, 9].

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