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Spoken language identification consists in recognizing a language based on a sample of speech from an unknown speaker. The traditional approach for this task mainly considers the phonothactic information of languages. However, for marginalized languages - languages with few speakers or oral languages without a fixed writing standard -, this information is practically not at hand and consequently the usual approach is not applicable. In this paper, we present a method that only considers the acoustic features of the speech signal and does not use any kind of linguistic information. This method applies a wavelet transform to extract the acoustic features of the speech signal. The experimental results on a pairwise discrimination task among nine languages demonstrated that this approach considerably outperforms other previous methods based on the sole use of acoustic features.