Language recognition is typically performed with methods that exploit phonotactics--a phone recognition language modeling (PRLM) system. A PRLM system converts speech to a lattice of phones and then scores a language model. A standard extension to this scheme is to use multiple parallel phone recognizers (PPRLM). In this paper, we modify this approach in two distinct ways. First, we replace the phone tokenizer by a powerful speech-to-text system. Second, we use a discriminative support vector machine for language modeling. Our goals are twofold. First, we explore the ability of a single speech-to-text system to distinguish multiple languages. Second, we fuse the new system with an SVM PRLM system to see if it complements current approaches. Experiments on the 2005 NIST language recognition corpus show the new word system accomplishes these goals and has significant potential for language recognition.