Question: What are the models used for the baseline of the three NLP tasks?

Evidence:
raw_evidence: Baseline Results ::: Speech Synthesis
In our previous work on building speech systems on found data in 700 languages, BIBREF7, we addressed alignment issues (when audio is not segmented into turn/sentence sized chunks) and correctness issues (when the audio does not match the transcription). We used the same techniques here, as described above.
For the best quality speech synthesis we need a few hours of phonetically-balanced, single-speaker, read speech. Our first step was to use the start and end points for each turn in the dialogues, and select those of the most frequent speaker, nmlch. This gave us around 18250 segments. We further automatically removed excessive silence from the start, middle and end of these turns (based on occurrence of F0). This gave us 13 hours and 48 minutes of speech.
We phonetically aligned this data and built a speech clustergen statistical speech synthesizer BIBREF9 from all of this data. We resynthesized all of the data and measured the difference between the synthesized data and the original data using Mel Cepstral Distortion, a standard method for automatically measuring quality of speech generation BIBREF10. We then ordered the segments by their generation score and took the top 2000 turns to build a new synthesizer, assuming the better scores corresponded to better alignments, following the techniques of BIBREF7.
For speech recognition (ASR) we used Kaldi BIBREF11. As we do not have access to pronunciation lexica for Mapudungun, we had to approximate them with two settings. In the first setting, we make the simple assumption that each character corresponds to a pronunced phoneme. In the second setting, we instead used the generated phonetic lexicon also used in the above-mentioned speech synthesis techniques. The train/dev/test splits are across conversations, as described above.
We built neural end-to-end machine translation systems between Mapudungun and Spanish in both directions, using state-of-the-art Transformer architecture BIBREF14 with the toolkit of BIBREF15. We train our systems at the subword level using Byte-Pair Encoding BIBREF16 with a vocabulary of 5000 subwords, shared between the source and target languages. We use five layers for each of the encoder and the decoder, an embedding size of 512, feed forward transformation size of 2048, and eight attention heads. We use dropout BIBREF17 with $0.4$ probability as well as label smoothing set to $0.1$. We train with the Adam optimizer BIBREF18 for up to 200 epochs using learning decay with a patience of six epochs.
raw_evidence: We phonetically aligned this data and built a speech clustergen statistical speech synthesizer BIBREF9 from all of this data. We resynthesized all of the data and measured the difference between the synthesized data and the original data using Mel Cepstral Distortion, a standard method for automatically measuring quality of speech generation BIBREF10. We then ordered the segments by their generation score and took the top 2000 turns to build a new synthesizer, assuming the better scores corresponded to better alignments, following the techniques of BIBREF7.
For speech recognition (ASR) we used Kaldi BIBREF11. As we do not have access to pronunciation lexica for Mapudungun, we had to approximate them with two settings. In the first setting, we make the simple assumption that each character corresponds to a pronunced phoneme. In the second setting, we instead used the generated phonetic lexicon also used in the above-mentioned speech synthesis techniques. The train/dev/test splits are across conversations, as described above.
We built neural end-to-end machine translation systems between Mapudungun and Spanish in both directions, using state-of-the-art Transformer architecture BIBREF14 with the toolkit of BIBREF15. We train our systems at the subword level using Byte-Pair Encoding BIBREF16 with a vocabulary of 5000 subwords, shared between the source and target languages. We use five layers for each of the encoder and the decoder, an embedding size of 512, feed forward transformation size of 2048, and eight attention heads. We use dropout BIBREF17 with $0.4$ probability as well as label smoothing set to $0.1$. We train with the Adam optimizer BIBREF18 for up to 200 epochs using learning decay with a patience of six epochs.

Answers:
state-of-the-art Transformer architecture, Kaldi, speech clustergen statistical speech synthesizer
For speech synthesis, they build a speech clustergen statistical speech synthesizer BIBREF9. For speech recognition, they use Kaldi BIBREF11. For Machine Translation, they use a Transformer architecture from BIBREF15.