A novel non-homogeneous hidden Markov model for simulating and predicting monthly rainfall

Wang, Hui; Asefa, Tirusew; Sarkar, Abhra

doi:10.1007/s00704-020-03447-2

Cited by 7 publications

(4 citation statements)

References 35 publications

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“…In the processing of the speech signal, any speech recognition system can use Figure 1 to represent its general recognition principle. e most important modules of the speech recognition system are speech feature extraction and speech pattern matching [15].…”

Section: Relevant Principles Of Speech Recognitionmentioning

confidence: 99%

Hybrid Algorithm for English Translation Speech Recognition Based on Deep Learning Model and Clustering

Zhang

2022

Security and Communication Networks

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Speech recognition is the most important research direction in human-computer interaction. It is the key to the connection between human beings and machines and the expression of intelligence and automation in the information society. Taking English as the research object, using the related knowledge of speech recognition, it is based on the hidden Markov model technology of deep learning and clustering analysis algorithm and evaluated according to the cross-language English phonemic recognition system of sparse autoencoder (SA) method. By studying the speech recognition algorithm of the English translation, the influence of the speech recognition environment on the accuracy of speech recognition is confirmed. This provides a direction for humans to study speech recognition at a deeper level. Based on the language model of Transformer and the language model based on Seq2Seq, it sets different vocabularies, and the data are collected in the laboratory and outdoors, respectively, and the posttest template library is formed after collection. In the task of restoring phonetic symbols to English characters when phonemes are modeling units, the error rate is the lowest. The error rate on the test set reached 9.54%, which was 6.97 percentage points higher than that of the syllable modeling unit.

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Section: Relevant Principles Of Speech Recognitionmentioning

confidence: 99%

Hybrid Algorithm for English Translation Speech Recognition Based on Deep Learning Model and Clustering

Zhang

2022

Security and Communication Networks

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“…Finally, some alternative NHMM approaches and more advanced machine‐learning algorithms, as abundantly reported in the recent scientific literature, may lead to further improvement in regional climate simulations (Bulla et al., 2010; Holsclaw et al., 2016; Irrgang et al., 2021; Long et al., 2019; Wang et al., 2021). Further investigations are worthy of exploration in the future.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Increase of Future Summer Rainfall in the Middle and Lower Reach of the Yangtze River Basin Projected With a Nonhomogeneous Hidden Markov Model

Guo

Jiang

et al. 2022

Geophysical Research Letters

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Among the statistical downscaling tools available for regional climate simulation, the nonhomogeneous hidden Markov model (NHMM) is a powerful and efficient algorithm. It consists of establishing statistical relations between predictand and predictors through a hidden layer of Markov process and a covariate nonhomogeneous term as external forcing. Here, it is used to simulate summer daily precipitation in the middle and lower reach of the Yangtze River basin (MLYRB), including future projection. Results show that NHMM well identifies large‐scale circulation features that are physically consistent with regional rainfall patterns. MLYRB exhibits a general wetness for 1.5°C and 2°C global warming targets, with smaller (larger) increase for the west (east). Such changes correspond to increases in the occurrence frequency of synoptic weather patterns with stronger and more westward Western Pacific Subtropical High and stronger westerly jet. This contributes to moistening MLYRB with increasing occurrence frequency for those wetter rainfall patterns.

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“…That is, given the condition of the model λ, the sequence fO 1 ; O 2 ; : : : ; O t g will be in the Si state at time t. The following are the steps of iterative calculation of forward variables: 15 (1) Initialization: E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 1 2 ; 1 1 6 ; 1 3 8…”

Section: Fusion Hmmmentioning

confidence: 99%

“…That is, given the condition of the model λ, the sequence {O1,O2,…,Ot} will be in the Si state at time t. The following are the steps of iterative calculation of forward variables: 15 …”

Section: Song Recommendation Based On Fusion Hmmmentioning

confidence: 99%

(Retracted) Multimedia music image production combining HMM and song feature tags

Cui

Wang

2022

J. Electron. Imag.

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In the vast river of art and music, this wonderful flower of art has inherited culture through its expansion in horizontal space and continuation in vertical time since its birth. This research mainly discusses the filming of multimedia music images that integrates hidden Markov model (HMM) and song feature tags. Hidden Markov model is a statistical model based on hidden Markov process, which is mainly used for modeling stochastic processes. The editing structure, editing style, and the time and space performance of the editing should be considered in the editing process. When filming multimedia music images in the study of music history, there are not only the shooting pictures of the main body of the musical things but also the introduction of the environmental information generated by the musical things. Editing needs to consider whether each shot and each picture meets the actual research situation for the musical things to be described. It uses HMM to predict the song category based on the user's listening behavior records and song characteristics and then recommends the corresponding category of music to the user in real-time. Finally, the recommended results of several algorithms are verified on the experimental data set. It compares the difference between the traditional music algorithm and the HMM from different dimensions, such as accuracy, error rate, and recommended time efficiency. The accuracy of HMM for music is predicted to be 95%. The multinote recognition system of the piano uses the characteristics of the hidden Markov double random process to describe the statistical characteristics of the audio stream of notes. The proposed research will help promote the dissemination of music image information.

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A novel non-homogeneous hidden Markov model for simulating and predicting monthly rainfall

Cited by 7 publications

References 35 publications

Hybrid Algorithm for English Translation Speech Recognition Based on Deep Learning Model and Clustering

Hybrid Algorithm for English Translation Speech Recognition Based on Deep Learning Model and Clustering

Increase of Future Summer Rainfall in the Middle and Lower Reach of the Yangtze River Basin Projected With a Nonhomogeneous Hidden Markov Model

(Retracted) Multimedia music image production combining HMM and song feature tags

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