Can we get speech recognition tools to work on limited-memory devices? The Viterbi algorithm is a classic dynamic programming (DP) solution used to find the most likely sequence of hidden states in a Hidden Markov Model (HMM). While the algorithm finds universal application ranging from communication systems to speech recognition to bioinformatics, its scalability has been scarcely addressed, stranding it to a space complexity that grows with the number of observations. In this paper, we propose SIEVE (Space Efficient Viterbi), a reformulation of the Viterbi algorithm that eliminates its space-complexity dependence on the number of observations to be explained. SIEVE discards and recomputes parts of the DP solution for the sake of space efficiency, in divide-and-conquer fashion, without incurring a time-complexity overhead. Our thorough experimental evaluation shows that SIEVE is highly effective in reducing the memory usage compared to the classic Viterbi algorithm, while avoiding the runtime overhead of a naïve space-efficient solution.

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