On the relevance of language in speaker recognition
This work addresses the problem of language variability in speaker recognition systems, which is incremental as it builds on existing methods to explore a specific bilingual context.
The authors investigated the impact of language on speaker recognition by analyzing a bilingual dataset of 49 speakers in Spanish and Catalan, concluding that phonetic differences between languages significantly affect recognition performance.
This paper presents a new database collected from a bilingual speakers set (49), in two different languages: Spanish and Catalan. Phonetically there are significative differences between both languages. These differences have let us to establish several conclusions on the relevance of language in speaker recognition, using two methods: vector quantization and covariance matrices