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We present the effort of JHU-CLSP/HLTCOE and MIT Lincoln labs for NIST Language Recognition Evaluation (LRE) 2022. LRE22 consisted of a language detection task, i.e., determining whether a given target language was spoken in a speech segment. LRE22 focused on telephone and broadcast narrowband speech in African languages. Since LRE17, there has been large progress in neural embeddings, combined or not, with self-supervised models like Wav2Vec2. Therefore, one of our goals was to investigate these new models, i.e., ECAPA-TDNN, Res2Net or Wav2Vec2+ECAPA-TDNN, in the LRE scenario. In the fixed training condition, LRE22 target languages were only included in a small development set. Hence, we focused on tuning our models to exploit the limited data. For the open condition, we built a massive training set including African data, which improved Cprimary by 50% w.r.t. fixed. Wav2Vec2 embeddings were the best, outperforming ECAPA and Res2Net by 11 and 3%, respectively.