Using Phonologically Weighted Levenshtein Distances for the Prediction of Microscopic Intelligibility

Lionel Fontan (1, 2) , Isabelle Ferrané (1) , Jérôme Farinas (1) , Julien Pinquier (1) , Xavier Aumont (2)

1 IRIT-SAMoVA - Équipe Structuration, Analyse et MOdélisation de documents Vidéo et Audio
2 Archean Technologies (FRANCE)

This article presents a new method for analyzing Automatic Speech Recognition (ASR) results at the phonological feature level. To this end the Levenshtein distance algorithm is refined in order to take into account the distinctive features opposing substituted phonemes. This method allows to survey features additions or deletions, providing microscopic qualitative information as a complement to word recognition scores. To explore the relevance of the qualitative data gathered by this method, a study is conducted on a speech corpus simulating presbycusis effects on speech perception at eight severity stages. Consonantic features additions and deletions in ASR outputs are analyzed and put in relation with intelligibility data collected in 30 human subjects. ASR results show monotonic trends in most conso- nantic features along the degradation conditions, which appear to be consistent with the misperceptions that could be observed in human subjects.

Mots clés: Microscopic Intelligibility; ASR; Phonological Features; Levenshtein Distance

Domaines: Synthèse d'image et réalité virtuelle [cs.GR]; Traitement du signal et de l'image [eess.SP]; Traitement des images [eess.IV]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Intelligence artificielle [cs.AI]

Lionel Fontan, Isabelle Ferrané, Jérôme Farinas, Julien Pinquier, Xavier Aumont. Using Phonologically Weighted Levenshtein Distances for the Prediction of Microscopic Intelligibility. Annual conference Interspeech (INTERSPEECH 2016), Sep 2016, San Francisco, CA, United States. pp.650-654, ⟨10.21437/Interspeech.2016-431⟩. ⟨hal-01474904⟩