This page contains information on forced alignment tools available here at CLST and from other third parties. They are compared and evaluated. It is in no way a formal evaluation, but more an evaluation of experiences and intuitions while working with them.

Montreal Forced Aligner (MFA): https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner
“MFA is quite user-friendly and easy to use. There are several options, you can use the pre-trained models or you can train the models on your own data/speakers. Attached you will find some examples of why I am not too happy with MFA (especially with the pretrained models), but I am not quite sure how to get a better system. It is especially hard because some of the speakers reduce and the dictionary is not equipped to handle reduction. I have not formally evaluated MFA yet, so I cannot make a general statement about its performance. However looking at just a few files, it seems like that training on the speaker yields better results.” (Katherine Marcoux)
By default MFA trains the acoustic models to the stage of speaker-adapted triphones (HMM-GMM). The pretrained models on their website also seem to be HMM-GMM. I assume Katherine uses these pretrained models and trains speaker-adapted models using the pretrained as a basis?
They do have code to also train acoustic models based on DNNs (Kaldi's NNet2), but they say: “The DNN framework for the Montreal Forced aligner is operational, but may not give a better result than the alignments produced by the standard HMM-GMM pipeline. Preliminary experiments suggest that results may improve when the DNN model used to produce alignments is pre-trained on a corpus similar in quality (conversational vs. clean speech) and longer in length than the test corpus.” –> https://montreal-forced-aligner.readthedocs.io/en/latest/alignment_techniques.html#deep-neural-networks-dnns (Mario Ganzeboom)

Kaldi ASR toolkit: http://kaldi-asr.org
HTK ASR toolkit: http://htk.eng.cam.ac.uk
“KALDI outperforms HTK, in general, but the output quality largely depends on the adequacy of the orthographic input.” (Louis ten Bosch, Katherine Marcoux)