Automatic speaker, age-group and gender identification from children’s speech
| Autor: | Saeid Safavi, Peter Jancovic, Martin J. Russell |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
Speech recognition Word error rate Identity (social science) 020206 networking & telecommunications 02 engineering and technology Speaker recognition 01 natural sciences Paralanguage Theoretical Computer Science Human-Computer Interaction Support vector machine Identification (information) 0103 physical sciences Stress (linguistics) 0202 electrical engineering electronic engineering information engineering Classification methods 010301 acoustics Software |
| Zdroj: | Computer Speech & Language. 50:141-156 |
| ISSN: | 0885-2308 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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