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fa-evaluation [2018/11/27 11:59]
mganzeboom |
fa-evaluation [2018/11/27 12:00] (current)
mganzeboom |
| **Montreal Forced Aligner (MFA)**: [[https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner]]\\ | **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." ([[https://www.ru.nl/english/people/marcoux-k/|Katherine Marcoux]])\\ | "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." ([[https://www.ru.nl/english/people/marcoux-k/|Katherine Marcoux]])\\ |
| By default the acoustic models are trained 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?\\ | 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: | 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 ([[https://www.ru.nl/english/people/ganzeboom-m/|Mario Ganzeboom]]) | "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 ([[https://www.ru.nl/english/people/ganzeboom-m/|Mario Ganzeboom]])\\ |
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| ===== Kaldi vs. HTK ===== | ===== Kaldi vs. HTK ===== |
