## 3 Experimental Results
### Speaker Identification Results  
In order to analyze the recognition performance of Speaker YAMNet for six speakers, we randomly sampled ten speech data of six speakers and concatenated them. We ignored the blank segments in the concatenated speech signals as a test set to assess the performance. The recognition precision, recall rate, and F-measure in the model, among which the number of speech frames for \"[PERSON]\" is 349, 449 frames for \"[PERSON]\", 347 frames for \"[PERSON]\", 365 frames for \"[PERSON]\", 412 frames for \"[PERSON]\", and 500 frames for \"[PERSON]\", as shown in Table 10.  
The precision rate, recall rate, and F-measure are key metrics in evaluating the performance of Speaker YAMNet. The precision rate \(P\), for instance, is a crucial measure that can be computed by  
\[P\%=N_{c}/\left(N_{c}+N_{fp}\right)\cdot 100\% \tag{4}\]  
where \(N_{c}\) represents the number of correctly identified sound frames; \(N_{fp}\) denotes the number of false positives. As shown in Equation (4), the larger the precision rate \(P\), the better the performance is. In addition, the recall rate \(R\) can be expressed by  
\[R\%=N_{c}/\left(N_{c}+N_{fp}\right)\cdot 100\% \tag{5}\]  
where \(N_{fp}\) represents the number of false positives and false negatives. As shown in Equation (5), the larger the recall rate \(R\), the better the performance is. The recall rate \(R\) denotes how many actual speakers can be correctly identified among the target speakers. That is, the more correctly identified speakers there are, the higher the recall rate is. Sometimes, a speaker identification system may obtain a high precision rate. However, the recall rate is low. The overall performance could be more acceptable. Conversely, a speaker identification system may result in a high recall rate, but the precision rate is low. The overall performance could be more satisfactory. Accordingly, the \(F\)-measure should be employed to measure the overall performance, given as  
\[F\text{-}measure=\left(2\cdot P\cdot R\right)/\left(P+R\right)\cdot 100\% \tag{6}\]  
The speaker identification results in terms of precision rate, recall rate, and \(F\)-measure are shown in Table 3. The target speech frames for the [PERSON] and [PERSON] speakers are all correctly identified; thus, these two speakers obtain the best performance. Taking the speaker [PERSON] as an example, the number of correctly identified speech frames is 448, while the number of false rejected frames is 5. So the recall rate is 98.56% (342/(342 + 5)). Because there is no falsely identified [PERSON] speaker's frame, the precision rate is 100%. The overall performance in terms of the \(F\)-measure is 99.27%. By observing the identification results, most incorrectly identified speech frames appeared at the boundary of changing speakers among the [PERSON] and [PERSON] speakers. The primary reason is that [PERSON] speaker's intonation and spectrum characteristics are similar to [PERSON]'s and [PERSON]'s, causing the Speaker YAMNet to fail to recognize the speech frame. The [PERSON] speaker was incorrectly identified to the other two speakers. The precision and recall rates of the Speaker YAMNet for the six speakers are both above 99%, which shows that the Speaker YAMNet can effectively identify the speech frames of the six speakers. The average precision rate, recall rate, and \(F\)-measure are 100%, 99.67%, and 99.83%, respectively. Accordingly, the Speaker YAMNet can accurately identify the target speakers for specific members. Figure 4 shows the waveform plots and identified speakers. While speaking, the Speaker YAMNet can simultaneously identify which speaker is speaking. The title displays the identified speakers, [PERSON] and [PERSON], above the waveform plot.