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We prefer ASR over other speech evaluation techniques based on forced alignment (FA) which is often used in second language learning.
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The authors of [15] present a system based on Viterbi forced alignment.
The signal processing to extract the formant and pitch values is based on a Linear Prediction Coefficients (LPCs) analysis of the segments considered as vowels in a Hidden Markov Model (HMM) based Viterbi forced alignment.
The signal processing to extract the formant and pitch values is based on a linear prediction coefficient (LPC) analysis of the segments considered as vowels in a hidden Markov model- (HMM-) based Viterbi forced alignment.
Both crystal configurations provide a well-defined azimuthal alignment, which is discussed based on force field simulations and a recently reported growth model.
molecular mechanics based on force fields.
The layout is based on force field clustering.
The forced alignment tool that we use is described in [26] and is based on the continuous speech recognizer in the source language.
The nucleotide alignment was generated based on the protein alignment using codon alignment.
This alignment was then fitted to a second alignment based on a needle alignment of E. coli Slt70 and the RvhB1 protein from Brucella suis [116].
The alignment size and RMSD based on this virtual alignment are termed the "virtual alignment size" and "virtual RMSD (vRMSD)", respectively.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com