Comparison of syllabification algorithms and training strategies for robust word count estimation across different languages and recording conditions

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu



  • Administrative Headquarters of the Max Planck Society


Word count estimation (WCE) from audio recordings has a number of applications, including quantifying the amount of speech that language-learning infants hear in their natural environments, as captured by daylong recordings made with devices worn by infants. To be applicable in a wide range of scenarios and also low-resource domains, WCE tools should be extremely robust against varying signal conditions and require minimal access to labeled training data in the target domain. For this purpose, earlier work has used automatic syllabification of speech, followed by a least-squares-mapping of syllables to word counts. This paper compares a number of previously proposed syllabifiers in the WCE task, including a supervised bi-directional long short-term memory (BLSTM) network that is trained on a language for which high quality syllable annotations are available (a “high resource language”), and reports how the alternative methods compare on different languages and signal conditions. We also explore additive noise and varying-channel data augmentation strategies for BLSTM training, and show how they improve performance in both matching and mismatching languages. Intriguingly, we also find that even though the BLSTM works on languages beyond its training data, the unsupervised algorithms can still outperform it in challenging signal conditions on novel languages.


OtsikkoProceedings of Interspeech
TilaJulkaistu - 1 tammikuuta 2018
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaInterspeech - Hyderabad International Convention Centre, Hyderabad, Intia
Kesto: 2 syyskuuta 20186 syyskuuta 2018


NimiProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
KustantajaInternational Speech Communication Association
ISSN (painettu)2308-457X



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