You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: We closely followed the procedure in [16]. All experiments were performed on the TIMIT corpus [19]. We used the train-dev-tests plit from the Kaldi [20] TIMIT s5 recipe. We trained on the standard 462 speaker set with all SA utterances removed and used the 50 speaker dev set for early stopping. We tested on the 24 speaker core test set. All networks were trained on 40 mel-scale filter-bank features together with the energy in each frame, and first and second temporal differences, yielding in total 123 features per frame. Each feature was rescaled to have zero mean and unit variance over the training set. Networks were trained on the full 61-phoneset extended with an extra “end-of-sequence” token that was appended to each target sequence. Similarly, we appended an all-zero frame at the end of each input sequence to indicate the end of the utterance. Decoding was performed using the 61+1 phoneme set, while scoring was done on the 39 phoneme set.

Question: What considerations would authors need to take to extend this model to languages that have more phonemes than American English? (eg. Indian languages, Chinese, etc)
When authors need to take to extend this model to other languages or other datasets than TIMIT corpus, they should extend the phonemes set for some phonemes that are not included in American English. This applies to all procedures including training/decoding/scoring.