Automatic language recognition on spontaneous speech has experienced a rapid development in the last few years. This development has been in part due to the competitive technological Language Recognition Evaluations (LRE) organized by the National Institute of Standards and Technology (NIST). Until now, the need to have clearly defined and consistent evaluations has kept some real-life application issues out of these evaluations. In particular, all past NIST LREs have used exclusively conversational telephone speech (CTS) for development and test. Fortunately this has changed in the current NIST LRE since it includes also broadcast speech. However, for testing only the telephone speech found in broadcast data will be used. In real-life applications, there could be several more types of speech and systems could be forced to use a mix of different types of data for training and development and recognition. In this article, we have defined a test-bed including several types of speech data and have analyzed how a typical language recognition system works using different types of speech, and also a combination of different types of speech, for training and testing.

In this paper we briefly describe the BioSec multimodal biometric database and analyze its use in automatic text-dependent speaker recognition research. The paper is structured into four parts: a short introduction to the problem of text-dependent speaker recognition; a brief review of other existing databases, including monomodal text-dependent speaker recognition databases and multimodal biometric recognition databases; a description of the BioSec database; and, finally, an experimental section in which speaker recognition results on some of these databases are presented and compared, using the same underlying speaker recognition technique in all cases.