## 3 Results
### ASR Performance  
We tested the speech utterances of the pre/post-tests with two different ASR systems, i.e., the general-purpose gASR and a specific-purpose ASR created from scratch with Kaldi (kASR), to validate that the results using kASR were not casual. We considered a comparison with other Spanish ASR systems [26] not to be informative or fair to carry out, since our kASR system is not a general-purpose one, but a tailored ASR with a closed set of words related to minimal pairs. Table 4 shows the WER values obtained by both ASR systems used in the experimentation with two different sources of speech data (native and non-native). Regarding the native models (Table 4), the _All_ model included 41,000 utterances of the native speakers in the train set. The _Female_ model included 20,500 utterances of the five female native speakers in the train set. The _Male_ model included 20,500 utterances of the five male native speakers in the train set. The _Best1_, _Best2_, and _Best3_ models included 32,800 utterances (80%) of the total native speakers (4 females and 4 males) in the train set. These last three models were obtained by comparing the WER values of all possible 80%/20% combinations (train/test sets) of the native speakers (e.g., 4 female and 4 male native speakers for training: 80%, and 1 female and 1 male for testing: 20%), and choosing \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Train Model**} \\ \hline  & \multicolumn{2}{c}{**gASR**} & \multicolumn{6}{c}{**kASR**} \\ \hline  & & _All_ & _Female_ & _Male_ & _Best1_ & _Best2_ & _Best3_ \\ \hline _Native_ & 5.0 & 0.0024 & 3.10 & 1.55 & 0.14 & 0.14 & 0.23 \\ \hline _Non-native_ & 30.0 & 44.22 & 55.91 & 64.12 & 46.40 & 46.98 & 48.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: WER values (%) of the experiment’s ASR systems. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Production Tasks (First Attempt[Last Attempt])**} \\ \hline \#_Lis_ & & **[f]** & **[f:]** & **[l]** & **[r]** & **[rr]** & **[s]** & **[0]** & **[f]** & **[fu]** & **[xu]** & **TPR (\%)** \\ \hline
13\(|\)128 & [f] & 65\(|\)**170** & 79\(|\)47 & - & - & - & - & - & - & - & - & 45.1\%\(|\)78.3\% \\
3\(|\)125 & [fr] & 73\(|\)64 & 65\(|\)**137** & - & - & - & - & - & - & - & - & 47.1\%\(|\)68.2\% \\
9\(|\)105 & [l] & - & - & **177\(|\)253** & 45\(|\)31 & 37\(|\)14 & - & - & - & - & - & 68.3\% & 84.9\% \\
8\(|\)103 & [r] & - & - & 33\(|\)21 & 209\(|\)_289_ & 42\(|\)14 & - & - & - & - & - & 73.6\%\(|\)89.2\% \\
3\(|\)70 & [rr] & - & - & 22\(|\)9 & 44\(|\)22 & 189\(|\)252 & - & - & - & - & - & 74.1\%\(|\)89.0\% \\
6\(|\)146 & [s] & - & - & - & - & - & 58\(|\)**134** & 66\(|\)67 & - & - & - & 46.8\%\(|\)66.7\% \\
2\(|\)202 & [f] & - & - & - & - & - & 79\(|\)96 & **142\(|\)226** & 38\(|\)12 & - & - & 54.8\%\(|\)67.7\% \\
0\(|\)29 & [f] & - & - & - & - & - & - & 38\(|\)19 & **97\(|\)116** & - & - & 71.9\%\(|\)85.9\% \\
4\(|\)240 & [fu] & - & - & - & - & - & - & - & - & **62\(|\)138** & 62\(|\)106 & 50.0\%\(|\)56.6\% \\
5\(|\)186 & [xu] & - & - & - & - & - & - & - & - & 59\(|\)91 & **63\(|\)140** & 51.6\%\(|\)60.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix of production tasks at first and last attempt per word sequence (diagonal: right production tasks at first and last attempt per word sequence). the best three WER values (the lowest ones). On the other hand, the non-native test model consisted of 3696 utterances (33 participants \(\times\) 28 minimal pairs \(\times\) 2 words per minimal pair \(\times\) 2 tests). The 5.0% WER value reported by Google for their English ASR system for native speech [10] corresponds to our WER value for native speech data. Google training techniques are applied also for their ASR in other majority languages, such as Spanish. Regarding our kASR system, we achieved values lower than 5.0% for native speech for the specific battery of minimal pairs introduced in Section 2 (e.g., _All_ model: 0.0024%).