SIEVE: A Space-Efficient Algorithm for Viterbi Decoding
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A4 Artikkeli konferenssijulkaisussa
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Date
2022-06-10
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en
Pages
10
1136-1145
1136-1145
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SIGMOD 2022 - Proceedings of the 2022 International Conference on Management of Data, Proceedings of the ACM SIGMOD International Conference on Management of Data
Abstract
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.Description
Publisher Copyright: © 2022 ACM.
Keywords
divide-and-conquer, space efficiency, viterbi decoding
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Citation
Ciaperoni, M, Gionis, A, Katsamanis, A & Karras, P 2022, SIEVE: A Space-Efficient Algorithm for Viterbi Decoding . in SIGMOD 2022 - Proceedings of the 2022 International Conference on Management of Data . Proceedings of the ACM SIGMOD International Conference on Management of Data, ACM, pp. 1136-1145, ACM SIGMOD International Conference on Management of Data, Virtual, Online, United States, 12/06/2022 . https://doi.org/10.1145/3514221.3526170