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We present a miniaturized universal hardware module for acoustic pattern recognition in various types of multichannel sensor signals. The module implements configurable signal analysis (signal transforms, filter banks, statistical transforms) and a GMM-HMM recognizer. The main hardware components are a XC7A75T FPGA performing almost all the computations, a TMS320C6746 digital signal processor organizing...
We present a miniaturized universal hardware module for acoustic pattern recognition in various types of multi-channel sensor signals. The module implements a configurable signal analysis (signal transforms, filter banks, statistical transforms) and GMM-HMM recognizer. The main hardware components are a XC7A75T FPGA performing almost all the computations, a TMS320C6746 digital signal processor organizing...
This paper presents an analysis-by-synthesis approach for acoustic model adaptation. Using artificial speech data for speech recognition systems adaptation, has the potential to address the problem of data sparseness, to avoid speech recordings in real conditions and to provide the capability of performing large number of development cycles for Automatic Speech Recognition (ASR) systems in shorter...
All of the previous syllable based Automatic Speech Recognizers (ASRs) for the Amharic language are built by training a separate acoustic model for each of the 196 distinctly pronounced Consonant-Vowel (CV) syllable. In this paper, we will demonstrate that a smaller number of acoustic models are sufficient to build a syllable based, speaker independent, continuous, Amharic ASR. It is built for weather...
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