# MLLP-VRAIN Spanish ASR Systems for the Albayzin-RTVE 2020 Speech-to-Text Challenge: Extension
## 3 MLLP-VRAIN Systems
### Experiments and Results  
To carry out and evaluate our system development, we used the dev and test sets from the RTVE2018 database. For the experiments, we devoted our internally split _dev1-dev_ set [28] for development purposes, whilst _dev2_ and _test-2018_ were used to test ASR performance. Finally, _test-2020_ was the blind test used by the organisation to rank the participant systems. Table 3 provides basic statistics of these sets. First, we studied the perplexity (PPL) on the _dev1-dev_ set of all possible linear combinations for the three types of LMs considered in this work. Table 4 shows the PPLs of these interpolations, along with the optimum LM weights that minimized PPL in the _dev1-dev_ set. The Transformer LM provides significantly lower perplexities in all cases and, accordingly, takes very high weight values when combined with other LMs. Indeed, the TLM in isolation already delivers a strong perplexity baseline value of 63.3, while the maximum PPL improvement is just 6% relative when all three LMs are combined. Second, we tuned decoding parameters to provide a good WER-RTF tradeoff on _dev1-dev_, with the hard constraint of RTF \(<1\) to ensure real-time processing of the input. From these hyperparameters, we highlight, due to their relevance, a LM history recombination of 12, LM histogram pruning of 20, and TLM history limited to 40 words. At this point, we defined our participant off-line hybrid ASR system identified as _c3-offline_ (contrastive system no. 3), consisting of a fast pre-recognition + Voice Activity Detection (VAD) step to detect speech/non-speech segments as in [28], followed by real-time one-pass decoding with our BLSTM-HMM AM, using a Full Sequence Normalization scheme and a linear combination of the three types of LMs: n-gram, LSTM and Transformer. This system scored 12.3 and 17.1 WER points on test-2018 and test-2020, respectively. Next, as our focus was to develop the best-performing streaming-capable hybrid ASR system for this competition, we explored streaming-related decoding parameters to optimize WER on _dev1-dev_, using the BLSTM-HMM AM and a linear combination of all three LMs. From this exploration, a context window size of 1.5 s and \(\alpha=0.95\) was chosen for the WMA normalization technique. This configuration was used for our primary system, labelled _p-streaming_1500 ms_nlt_, that showed WER rates of 11.6 and 16.0 on _test-2018_ and _test-2020_, respectively. It is important to note that this system does not integrate any VAD \begin{table}
\begin{tabular}{c c c} \hline \hline
**LM Comb.** & **PPL** & **Weights (\%)** \\ \hline ng & 179.5 & - \\ ls & 98.4 & - \\ tf & 63.3 & - \\ ng + ls & 93.2 & 15 + 85 \\ ng + tf & 61.6 & 6 + 94 \\ ls + tf & 60.7 & 13 + 87 \\ ng + ls + tf & 59.5 & 5 + 10 + 85 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Perplexity (PPL) and interpolation weights, computed over the _dev1-dev_ set, of all possible linear combinations of n-gram (ng), LSTM (ls) and Transformer (tf) LMs. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Set** & **Duration (h)** & **Files** & \(d_{\mu}\) & \(\pm\) & \(\sigma\) & **RW (K)** \\ \hline _dev1-dev_ & 11.9 & 10 & 4267 & \(\pm\) & 1549 & 120 \\ _dev2_ & 15.2 & 12 & 4564 & \(\pm\) & 1557 & 149 \\ _test-2018_ & 39.3 & 59 & 2395 & \(\pm\) & 1673 & 377 \\ _test-2020_ & 78.4 & 87 & 2314 & \(\pm\) & 1576 & 519 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Basic statistics of RTVE development and tests sets, including our internally split _dev1-dev_ set: total duration (in hours), number of files, average duration of samples in seconds plus-minus standard deviation (\(d_{\mu}\pm\sigma\)), and running words (RW) in thousands (K). module. Instead, this task is left for the decoder to carry out via the implicit non-speech model of the BLSTM-HMM AM. A small change in the configuration of the primary system, consisting in the removal of the LSTM LM from the linear interpolation, led to the contrastive system no. 1, identified as _c1-streaming_1500 ms_nt_.