A novel approach to perform context‐based automatic spoken document retrieval of political speeches based on wavelet tree indexing

Gupta, Anishka; Yadav, Divakar

doi:10.1007/s11042-021-10800-8

Cited by 11 publications

(3 citation statements)

References 27 publications

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“…The segmentation accuracy of this method can reach 99%, and the word segmentation speed can reach 30 characters per second. The literature [7] used a combination of word shape matching and string frequency statistics to achieve automatic word segmentation in English, which is suitable for automatic abstraction and document retrieval. The literature [8] proposed English name recognition based on statistics.…”

Section: Related Workmentioning

confidence: 99%

Simulation of English part of speech classification based on artificial intelligence and additive logistic regression

Jia

2023

Preprint

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English part-of-speech classification technology is a technology that can process text data, can effectively solve the problem of messy data in text information categories, make data structured and organized, and facilitate people to obtain effective information implicit in the text. This article transforms the original polynomial distribution into a generalized linear model and uses logistic regression algorithm for specific implementation. Moreover, the model proposed in this paper inherits the good explanatory characteristics of the decision tree, and it locally uses logistic regression to fit the data, which greatly improves the function space that logistic regression can fit. In addition, due to changes in the decision theory of logistic regression leaf nodes, the corresponding tree branch theory also needs to be changed accordingly. Finally, this paper designs experiments to study the performance of the model constructed in this paper. The research results show that the model constructed in this paper has high accuracy in the extraction and classification of English part of speech features.

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Section: Related Workmentioning

confidence: 99%

Simulation of English part of speech classification based on artificial intelligence and additive logistic regression

Jia

2023

Preprint

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“…[2,6]), which allows O(1) access. However, hash tables have several issues from an implementation point of view [17].…”

Section: Binary Search Tree (Bst)mentioning

confidence: 99%

“…As mentioned in Section 3.1, software implementations typically use hash maps for ASL. These are useful when a large number of active states are permitted so that we have least collision resolutions needed [17]. Hardware platforms on the other hand are resource-constrained.…”

Section: Asl Implementation: Hash Table Versus Bst-mhmentioning

confidence: 99%

Reduced Memory Viterbi Decoding for Hardware-accelerated Speech Recognition

Raj

Akhil

Chandrachoodan

2022

ACM Trans. Embed. Comput. Syst.

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Large Vocabulary Continuous Speech Recognition (LVCSR) systems require Viterbi searching through a large state space to find the most probable sequence of phonemes that led to a given sound sample. This needs storing and updating of a large Active State List (ASL) in the on-chip memory (OCM) at regular intervals (called frames), which poses a major performance bottleneck for speech decoding. Most works use hash tables for OCM storage while beam-width pruning to restrict the ASL size. In order to achieve a decent accuracy and performance, a large OCM, numerous acoustic probability computations and DRAM accesses are incurred. We propose to use a binary search tree (BST) for ASL storage and a max heap (MH) data structure to track the worst cost state and efficiently replace it when a better state is found. With this approach, the ASL size can be reduced from over 32K to 512 with minimal impact on recognition accuracy for a 7000-word vocabulary model. This, combined with a caching technique for acoustic scores, reduced the DRAM data accessed by 31 × and the acoustic probability computations by 26 ×. The approach has also been implemented in hardware on a Xilinx Zynq FPGA at 200 MHz using the Vivado SDS compiler. We study the trade-offs between the amount of OCM used, and word error rate (WER) and decoding speed to show the effectiveness of the approach. The resulting implementation is capable of running faster than real-time with 91% lesser block-RAMs.

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Simulation of English part-of-speech classification based on artificial intelligence and additive logistic regression

Jia

2023

Soft Comput

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A novel approach to perform context‐based automatic spoken document retrieval of political speeches based on wavelet tree indexing

Cited by 11 publications

References 27 publications

Simulation of English part of speech classification based on artificial intelligence and additive logistic regression

Simulation of English part of speech classification based on artificial intelligence and additive logistic regression

Reduced Memory Viterbi Decoding for Hardware-accelerated Speech Recognition

Simulation of English part-of-speech classification based on artificial intelligence and additive logistic regression

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