In this paper, we propose a speech decoder, called SPeech lIght decoDER (SPIDER), for extracting the best decoding hypothesis from a search space constructed using weighted finite-state transducers. Despite existence of many speech decoders, these decoders are quite complicated as they take into consideration many design goals, such as extraction of N-best decoding hypotheses and generation of lattices. This makes it difficult to learn these decoders and test new ideas in speech recognition that often require decoder modification. Therefore, we propose in this paper a simple decoder supporting the primitive functions required for achieving real-time speech recognition with state-of-the-art recognition performance. This decoder can be viewed as a seed for further improvements and addition of new functionalities. Experimental results show that the performance of the proposed decoder is quite promising when compared with two other speech decoders, namely HDecode and Sphinx3.
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