AHUMADA: a large speech corpus in Spanish for speaker identification and verification

Abstract

Speaker recognition is a major task when security applications through speech input are needed. Regarding speaker identity, several factors of variability must be considered: (a) factors concerning peculiar intra-speaker variability (manner of speaking, inter-session variability, dialectal variations, emotional condition, etc.) or forced intra-speaker variability (Lombard effect, cocktail-party effect), and (b) factors depending on external influences (kind of microphone, channel effects, noise, reverberation, etc). To cope with all these variability sources, a specific speech database called AHUMADA has been designed and collected for speaker recognition tasks in Castilian Spanish. AHUMADA incorporates six different recording sessions, including both in situ and telephone speech recordings. A total of 104 male speakers uttered isolated digits, digit strings, phonologically balanced short utterances, phonologically and syllabically balanced read text and more than one minute of spontaneous speech, so about 15 GB of speech material is available. Speaker verification results, concerning the available variability sources are also presented.