The phrase "average identification of" is correct and usable in written English.
It can be used in contexts where you are discussing the typical or mean recognition or categorization of something.
Example: "The study focused on the average identification of species among the participants in the biodiversity workshop."
Alternatives: "mean recognition of" or "typical identification of".
When using generalised features calculated on the basis of the VOC response curves an average identification rate of 87% was achieved.
Figure 8 shows the average identification throughput of tag acquisition versus SNR for K=100 and N=1 000.
Table6 shows that by using '1s.5u' datasets, the average identification rates of '24 NNs', '12 NNs', and '6 NNs' configurations for matched conditions were 88.4%, 90.3%, and 90.3%.
Table9 shows that by using '1s.5u' datasets, the average identification rates of '24 NNs', '12 NNs', and '6 NNs' configurations for known conditions were 90.5%, 91.4%, and 91.1%.
For the first testing scheme, the average identification rates of 'linear 8-1-0' and 'linear 4-1-4' frame selections for matched conditions were 69.7% and 76.2% by using '1s.5u' datasets and 78.5% and 82.1% by using '3s.15u' datasets.
The average identification accuracy of all the music is 86.8%, and the kappa statistical parameter κ that estimates the overall agreement is significant (κ = 0.800, P<0.001).
After training, the average identification accuracy of the two categories (defined by the training procedure as A for tokens 1 3 and B for tokens 5 7) increased from 62% to 88% (t = −6.98, P < 2 × 10−6), and the average RT decreased from 964 to 787 ms (t = 4.18, P < 6 × 10−4).
By using multiple NNs and 15 pairs of utterances by 1 speaker from 3 positions, we could reach 93.7% of average identification rate, which was 42.2% of ERR relative to the use of CMN.
Then, by using combined dataset containing 15 pairs of utterances by one speaker from three positions in a room, we could reach 93.7% of average identification rate (three known and two unknown positions), which was 42.2% of ERR relative to the use of cepstral mean normalization (CMN).
By using multiple NNs ('24 NNs') and 15 pairs of utterances by one speaker from three positions, we could reach 93.7% of average identification rate over three known and two unknown positions, which was 42.2% of ERR relative to the use of CMN.
