Cluster adaptive training with factorized decision trees for speech recognition

2013 ◽  
Author(s):  
Kai Yu ◽  
Hainan Xu
Keyword(s):  
Speech Recognition ◽  
Decision Trees ◽  
Adaptive Training

scholarly journals Speech recognition based on statistical models including multiple phonetic decision trees

2011 ◽  
Vol 32 (6) ◽  
pp. 236-243
Author(s):  
Sayaka Shiota ◽  
Kei Hashimoto ◽  
Heiga Zen ◽  
Yoshihiko Nankaku ◽  
Akinobu Lee ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Decision Trees ◽  
Statistical Models

Performance of forced-alignment algorithms on children’s speech

2020 ◽  
Author(s):  
Tristan Mahr ◽  
Visar Berisha ◽  
Kan Kawabata ◽  
Julie Liss ◽  
Katherine Hustad
Keyword(s):  
Speech Recognition ◽  
Gold Standard ◽  
Alignment Accuracy ◽  
Speech Sample ◽  
Older Children ◽  
Adaptive Training ◽  
Alignment Algorithms ◽  
Child Speech ◽  
Speech Recognition Engine ◽  
Children's Speech

Aim. We compared the performance of five forced-alignment algorithms on a corpus of child speech.Method. The child speech sample included 42 children between 3 and 6 years of age. The corpus was force-aligned using the Montreal Forced Aligner with and without speaker adaptive training, triphone alignment from the Kaldi speech recognition engine, the Prosodylab Aligner, and the Penn Phonetics Lab Forced Aligner. The sample was also manually aligned to create gold-standard alignments. We evaluated alignment algorithms in terms of accuracy (whether the interval covers the midpoint of the manual alignment) and difference in phone-onset times between the automatic and manual intervals.Results. The Montreal Forced Aligner with speaker adaptive training showed the highest accuracy and smallest timing differences. Vowels were consistently the most accurately aligned class of sounds across all the aligners, and alignment accuracy increased with age for fricative sounds across the aligners too. Interpretation. The best-performing aligner fell just short of human-level reliability for forced alignment. Researchers can use forced alignment with child speech for certain classes of sounds (vowels, fricatives for older children), especially as part of a semi-automated workflow where alignments are later inspected for gross errors.

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