Fusing Text-dependent Word-level i-Vector Models to Screen 'at Risk' Child Speech
Fusing Text-dependent Word-level i-Vector Models to Screen 'at Risk' Child Speech
复制标题
融合文本相关的词级 i-Vector 模型来筛查“有风险”的儿童言语
DOI:
10.21437/interspeech.2018-1465
复制
发表时间:
2018
影响因子:
1.8
通讯作者:
J. Hansen
中科院分区:
文献类型:
--
作者:
Prasanna V. Kothalkar;J. Rudolph;C. Dollaghan;Jennifer McGlothlin;T. Campbell;J. Hansen
Speech sound disorders (SSDs) are the most prevalent type of communication disorder among preschoolers. The earlier an SSD is identified, the earlier an intervention can be provided to potentially reduce the social/academic impact of the disorder. The challenge, lies in early identification of such disorders. In this study 29 carefully selected words were produced by 165 children from 3-6 years of age. The audio recordings, were collected by parents using a mobile application /platform. ”Ground truth” child status as ’typically developing’ vs ’at risk’ was based on a percentage of consonants correct-revised growth curve model. State-of-the-art speech processing/speaker recognition models were employed along with our clinical group verification framework. Results showed that text-dependent i-Vector models were superior to both text dependent and text-independent Gaussian Mixture Models (GMMs) for correct classification of children. Fusing individual word, i-Vector models provides insight into word and consonant groupings that are more indicative of ’at risk’ child speech.
DOI:
10.21437/slpat.2016-16
发表时间:
2016-09
期刊:
Workshop on Speech and Language Processing for Assistive Technologies
影响因子:
--
作者:
Wang J;Kothalkar PV;Kim M;Yunusova Y;Campbell TF;Heitzman D;Green JR
通讯作者:
Green JR
DOI:
10.1044/1092-4388(2007/077
发表时间:
2007
期刊:
Journal of speech, language, and hearing research : JSLHR
影响因子:
--
作者:
Campbell,ThomasF;Dollaghan,Christine;Janosky,JanineE;Adelson,PDavid
通讯作者:
Adelson,PDavid