## 5 Optimal Visual Unit Set Sizes  
Figure 5 plots word correctness on the \(y\)-axis for all 12 speakers with error bars showing \(\pm\) one standard error (se). The \(x\)-axis shows the number of visual units. In green we plot mean weighted guessing over all speakers for each viseme set. Individual speaker variations are in Appendix A, Figures 11-16.  
It is important in this case to weight the chance of guessing by visual homophenes as these vary by the size of the visual unit set. Visual unit sets which contain fewer visual units produce sequences of visual units which represent more than one word. These are homophenes. The effect of homophenes can be seen on the left side of Figure 5 and the graphs in Appendix A with visual unit sets with fewer than 11 visual units where homophenes become noticeable and language model can no longer correct these confusions.  
An example of a homophene in the RMAV data are the words 'tonnes' and'since'. If one uses Speaker 1's 10-visual unit P2V map, both words transcribe into visual units as '/ /In practice a language model, or word lattice, will tend to reduce such confusions since the lattice models the probability of word \(N\)-grams which means that probable combinations such as \"metric tonnes\" will be favored over \"metric since\" [23].  
We see all our word correctness scores are significantly above guessing albeit still low. There is variation between speakers, but there is a clear overall trend. Superior performance is to be found with larger numbers of visual units. An important point is some authors report viseme accuracy instead of word correctness [42]. This is unhelpful as it masks the effect of homophenous words on performance. Had we reported this then the positive effect of larger visual unit sets would not be visible.  
In Figure 5 we highlight in red the class sets which, for any speaker, have shown a significant classification improvement (with non-overlapping error bars) over the adjacent set of units on its right side along the \(x\)-axis. Error bars overlap once the correctness is averaged so Table 5 lists these combinations for each speaker. These red points show where we can identify the pairs of classes which, when merged into one class, significantly improve classification. If we refer to the speaker demographic factors such as gender or age, we find no apparent pattern through these visual unit combinations. So, we have further evidence to reinforce the idea that all speakers have a unique visual speech signal, [52]. In [53] this is suggested to be due to how the trajectory between visual units varies by speaker, due to such things as rate of speech [54]. This is how difficult finding a set of cross-speaker visual units can be when phonemes need alternative groupings for each individual [27].  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Speaker** & **Set No** & \(\mathbf{V_{i}}\) & \(\mathbf{V_{j}}\) & **Set No** & \(\mathbf{V_{n}}\) \\ \hline Sp01 & 35 & /s/ /r/ & /\(\delta\)/ & 34 & /s/ /r/ /\(\delta\)/ \\ Sp02 & 22 & /d/ & /z/ /y/ & 21 & /d/ /z/ /y/ \\ Sp03 & 34 & /b/ /t/ & /3/ & 33 & /b/ /t/ /\(\delta\)/ \\ Sp03 & 31 & /s/ /b/ /t/ & /z/ & 30 & /3/ /b/ /t/ / /z/ \\ Sp03 & 25 & /p/ /r/ & /\(\eta\)/ & 24 & /p/ /r/ /\(\eta\)/ \\ Sp05 & 17 & /ae/ & /eh/ & 16 & /ae/ /eh/ \\ Sp06 & 35 & /ae/ /\(\lambda\)/ & /iy/ & 34 & /ae/ / /iy/ \\ Sp09 & 12 & /b/ /w/ /v/ & /d3/ /hh/ & 11 & /b/ /w/ /v/ /d3/ /hh/ \\ Sp12 & 36 & /\(\lambda\)/ & /\(\beta\)/ & 35 & /\(\lambda\)/ /\(\beta\)/ \\ \hline \hline \end{tabular}
\end{table}
Table 5: visual unit class merges which improve word classification in correctness; \(V_{n}=V_{i}+V_{j}\).  
Figure 5: All-speaker mean word classification correctness \(C\pm 1\)_se_.