## 3 Systems
### Spoken Term Detection
#### 3.1.1 Kaldi-Based DNN with Data Augmentation System (Kaldi DNN + DA)  
This system, whose architecture is presented in Figure 1, is based on an LVCSR system constructed with the open-source Kaldi toolkit [74]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Team ID** & **Research Institution** & **Systems** & **Task** & **Type of System** \\ \hline \multirow{3}{*}{CENATAV} & Voice group, & Kaldi DNN + DA & STD & LVCSR \\  & Advanced Technologies & Kaldi SGMM + DA & STD & LVCSR \\  & Application Center, & Kaldi DNN & STD & LVCSR \\  & Cuba & Kaldi SGMM & STD & LVCSR \\ \hline \multirow{3}{*}{AUDIAS} & Universidad & E2E + LA & QbE STD & LD end-to-end \\  & Autonoma & E2E & QbE STD & LI end-to-end \\  & de Madrid, Spain & E2E + ZNORM & QbE STD & LI end-to-end \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Participants in the ALBAYZIN 2020 SoS along with the systems submitted. Figure 1: Architecture of the Kaldi DNN + DA system. First, a VAD is employed to remove noisy and voiceless segments from the original speech signals. This VAD was developed with the open-source machine learning framework Pytorch as an artificial neural network that consists of a single unidirectional long short-term memory (LSTM) recurrent layer and one fully connected layer. The input features for this VAD are 12 Mel frequency cepstrum coefficients (MFCCs) along with their first derivatives. Data employed for VAD training consist of the training data provided by the evaluation organizers. Then, the LVCSR module based on Kaldi takes the speech segments as input and produces word lattices as output. The system's design makes use of the s5 Wall Street Journal (WSJ) recipe in Kaldi [171]. The acoustic features are 13 MFCCs with cepstral mean and variance normalization (CMVN) applied to reduce the effects of the channel, and appended, just for the flat initialization of the acoustic model, with their delta and acceleration coefficients. Some other transformations, such as linear discriminant analysis (LDA), maximum likelihood linear transform (MLLT) and feature-space maximum likelihood linear regression (fMLLR) are also applied to obtain more robust features. The acoustic model training starts with a flat initialization of context-independent phone hidden Markov models (HMMs). Then, several re-training and alignment of acoustic models are carried out to produce context-dependent phone HMMs, following the standard procedures of the Kaldi s5 WSJ recipe. These phone models consist of three HMM states, each in a tied-pdf cross-word tri-phone context with Gaussian mixture models (GMMs). Then, the subspace Gaussian mixture model (SGMM) is employed on top of the GMM-HMM model for speaker adaptation, as described in [172], using fMLLR features and sharing the same Gaussian model. The GMM-HMM is also employed to produce the alignments needed to the DNN-based acoustic model (DNN-HMM). The DNNs consist of two hidden layers with 300 nodes each. The number of spliced frames is 9 to produce 360 dimensional vectors as input to the first layer. The output layer is a soft-max layer representing the log-posteriors of the context-dependent HMM states. The acoustic model training data comprise the following data: the TC-STAR evaluation package data recorded from 2005 to 2007, which amount to about 27 h of speech; a subset of the _dev1_ set of the RTVE data, which amounts to about 14 h of speech; the LibriVox-Bailen data, which consist of audiobook speech and amount to about 7 h of speech; and the MAVIR training data, which amount to more than 4 h of speech. There are 52 h of speech material in total in the four datasets. Overlapped speech is removed from the _dev1_ set of RTVE, and thus eventually 51 h of speech are used for acoustic model training. The data used for language model training consist of the text transcriptions of the acoustic model training data, which contain 163,000 word occurrences.