## 4 Automatic Within-Show Diarization Baseline Systems
### Baseline System Description  
To provide a fair study, all experiments are performed with two baseline automatic diarization systems. Both systems perform the diarization in two steps: a segmentation process, that splits the audio stream into (possibly overlapping) segments and a clustering process, that groups the segments into clusters: one cluster per speaker. The LIUM Voice Activity Detection (_VAD_) system is used to segment the audio stream by discarding non-speech segments (silence, noise, breathing, etc.). This VAD, based on stacked LSTM [3], is implemented in the S4D open-source framework [46]. The output of the network is smoothed by removing non-speech segments shorter than 50 ms and speech segments shorter than 25 ms. The clustering is then performed in four steps: (i) a first hierarchical agglomerative clustering (HAC) is performed on vectors of 13 MFCC using the BIC criteria [46]; (ii) a Viterbi decoding is then used to smooth the segment borders along the audio stream; (iii) \(x\)-vectors are extracted from each segment and averaged to provide a single x-vector per BIC-HAC cluster; (iv) a second (final) HAC clustering is done by using \(x\)-vectors. The distance matrix used for this clustering is computed using a PLDA scoring [47]. The only difference between both baseline systems lays in the \(x\)-vector extractor. The SincNet Diarization System (_SincDS_) uses the SincNet extractor described in Table 2. The dimension of the produced \(x\)-vectors is 100. The input of the _SincDS_ model is 80 dimensional MFCC extracted on a sliding window of 25 ms with a shift of 10 ms. The ResNet Diarization System (_ResDS_) uses a Half-ResNet34 extractor (see Table 3) to produce embeddings of size 256. As input, the _ResDS_ model takes 80 dimensional Mel filter bank coefficient vectors extracted every 10 ms on sliding windows of 25 ms. Both MFCC and Mel-spectrogram means and variances are normalized. Training of both networks is performed using an Adam optimizer with a Cyclic Triangular scheduler and cycles of length 20 steps. The learning rate oscillates between 1e-8 and 1e-3. One epoch corresponds to 100 audio chunks for each training speaker. Batches of 256 chunks are balanced across speakers and data augmentation is performed by randomly applying a single transformation among: noise addition, reverb addition, compression (GSM, ULAW, MP3 or Vorbis coded), phone filtering and pass-band filtering. Time and frequency masking are then applied for each chunk. Both networks are implemented using the SIDEKIT open-source framework [48] while the remaining of the system makes use of S4D [46]. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Layer Name** & **Structure** & **Output (\(C\times F\times T\))** \\ \hline Input & - & \(1\times 80\times T\) \\ \hline \multirow{4}{*}{MFCC} & SincNet [80; 251; 1] & \\  & 1D-Conv [60; 5; 1] & \\  & 1D-Conv [60; 5; 1] & \\ \hline Conv1D-1 & [512; 5; 1] & \\ Conv1D-2 & [512; 3; 2] & \\ Conv1D-3 & [512; 3; 3] & \\ Conv1D-4 & [512; 1, 1] & \\ Conv1D-5 & [1536, 1, 1] & \\ StatePooling & & \\ Linear-1 & [3072, 100] & 100 \\ Fully-Connected-1 & [100; 512] & 512 \\ Fully-Connected-2 & [512; 512] & 512 \\ Linear-2 & [512; 659] & 659 \\ SoftMax & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Architecture of the SincNet \(x\)-vector extractor. Dropout is used for all layers except the Linear layers. The activation function for Convolutional and Fully Connected layers is LeakyRelu. (\(C,F,T\)) stand for Channels, Features, Time. Applying two consecutive clustering steps makes the application of active correction more complex but removing one of the steps degrades the performance of the baseline system. Thus we chose to keep the two consecutive clustering steps but to only apply active correction at the second clustering step while considering the BIC-HAC clusters as frozen.