Automatic Speaker, Age-group and Gender Identification from Children's Speech
Research output: Contribution to journal › Article
Colleges, School and Institutes
A speech signal contains important paralinguistic information, such as the identity, age, gender, language, accent, and the emotional state of the speaker. Automatic recognition of these types of information in adults' speech has received considerable attention, however there has been little work on children's speech. This paper focuses on speaker, gender, and age-group recognition from children's speech. The performances of several classification methods are compared, including Gaussian Mixture Model - Universal Background Model (GMM-UBM), GMM - Support Vector Machine (GMM-SVM) and i-vector based approaches. For speaker recognition, error rate decreases as age increases, as one might expect. However for gender and age-group recognition the effect of age is more complex due mainly to consequences of the onset of puberty. Finally, the utility of different frequency bands for speaker, age-group and gender recognition from children's speech is assessed.
|Journal||Computer Speech and Language|
|Early online date||9 Jan 2018|
|Publication status||E-pub ahead of print - 9 Jan 2018|
- speaker recognition, gender identification, children's speech, age-group identification, Guassian Mixture Model, Support Vector Machine, i-vector