# Cascade or Direct Speech Translation? A Case Study +
## 4 Baseline Models
### Cascade Models
#### 4.1.1 Speech Recognition  
Two speech recognition architectures, based on end-to-end models and Kaldi-based systems, were trained and evaluated on the newly compiled corpus.  
The end-to-end speech recognition systems were based on the Deep Speech 2 architecture [49] for both languages and were set up with a sequence of two layers of 2D  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Range** & **Train** & **Dev** & **Test** \\ \hline
300–400 & 1 & 0 & 0 \\
200–250 & 1 & 0 & 0 \\
150–200 & 1 & 0 & 0 \\
100–150 & 7 & 0 & 0 \\
50–100 & 288 & 1 & 5 \\ MAX–50 & 5058 & 37 & 83 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Outlier distribution per duration range (seconds). MAX indicates the maximum value excluding outliers as computed for each partition.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**EU**} & \multicolumn{3}{c}{**ES**} \\ \cline{2-7}  & **Train** & **Dev** & **Test** & **Train** & **Dev** & **Test** \\ \hline Number of speakers & 151 & 104 & 127 & 175 & 130 & 155 \\ Median speaker time & 18.62 & 0.43 & 1.23 & 58.96 & 72.02 & 1.28 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Number of speakers and median speaker times (minutes).  
convolutional neural networks followed by five layers of bidirectional GRU layers and a fully connected final layer. The output corresponds to a softmax function, which computes a probability distribution over characters. The input consisted of Mel-scale-based spectrograms, which were dynamically augmented through the SpecAugment technique [50]. The models were trained using only audio lasting less than 40 s, due to training memory constraints, with a learning rate of 0.0001 annealed by a constant factor of 1.08 for a total of 60 training epochs, and Stochastic Gradient Descent (SGD) was used as the optimiser.  
The Kaldi recognition systems were built with the nnet3 DNN setup using the so-called chain acoustic model based on a Factorised Time-Delay Neural Network (TDNN-F) [51], which reduces the number of parameters of the network by factorising the weight matrix of each TDNN layer into the product of two low-rank matrices. Our TDNN-F models consisted of 16 TDNN-F layers with an internal cell dimension of 1536, a bottleneck dimension of 160, and a dropout schedule of \"0,[EMAIL_ADDRESS],[EMAIL_ADDRESS],0\". The number of training epochs was set to four, with a learning rate of 0.00015 and a minibatch size of sixty-four. The input vector corresponded to a concatenation of 40-dimensional high-resolution MFCC, augmented through speed (using factors of 0.9, 1.0, and 1.1) [52] and volume (with a random factor between 0.125 and two) [53] perturbation techniques, as well as the appended 100-dimensional iVectors.  
Language models were five-gram models with modified Kneser-Ney smoothing, estimated with the KenLM toolkit [54], and were used as either a component of Kaldi-based systems or to rescore the end-to-end models' hypotheses.  
The capitalisation models were trained on the mintzai-ST corpus with the recasing tools provided by the Moses open-source toolkit [47], using default parameters that included phrases of length one and a trigram KenLM language model.  
Finally, the punctuation module consisted of a Bidirectional Recurrent Neural Network (BRNN) model, which takes advantage of Gated Recurrent Units (GRU) as recurrent layers and an attention mechanism to further increase its capacity to identify relevant parts of the context for punctuation decisions. The models were built using the Punctuator2 toolkit [55] and trained on generic text corpora crawled from digital newspapers and acoustic data from the broadcast news domain [56]. The text corpora were composed of 161.8 million words for Spanish and 139.4 million words for Basque, whilst the acoustic corpus included 200 h of annotated audio per language.