This paper introduces a stacked architecture that uses a time delay neural network (TDNN) to model long-term patterns for spoken language identification. The first component of the architecture is a feed-forward neural network with a bottleneck layer that is trained to classify context-dependent phone states (senones). The second component is a TDNN that takes the output of the bottleneck, concatenated over a long time span, and produces a posterior probability over the set of languages. The use of a TDNN architecture provides an efficient model to capture discriminative patterns over a wide temporal context. Experimental results are presented using the audio data from the language i-vector challenge (IVC) recently organized by NIST. The proposed system outperforms a state-of-the-art shifted delta cepstra i-vector system and provides complementary information to fuse with the new generation of bottleneck-based i-vector systems that model short-term dependencies.
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