Fuzzy Clustering of Single-View Incomplete Data Using a Multiview Framework

Choudhury, Suvra Jyoti; Pal, Nikhil R.

doi:10.1109/tfuzz.2022.3173673

Cited by 6 publications

(1 citation statement)

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“…However, dealing with incomplete instances is a common phenomenon when processing real-world datasets (Liu and Letchmunan, 2024 ). Typically, to ensure the completeness of data collection, methods such as fuzzy clustering, interpolation, multisensory information fusion, and similarity measurement are employed during data preprocessing to fill in missing data and improve the performance of machine learning (Choudhury and Pal, 2022 ; Liu, 2023 , 2024 ). However, in some special fields, simulating missing data can become exceptionally cumbersome, or the supplemented missing data may differ significantly from real data.…”

Section: Introductionmentioning

confidence: 99%

Assessment and analysis of accents in air traffic control speech: a fusion of deep learning and information theory

Pan,

Zhang,

Zhang

et al. 2024

Front. Neurorobot.

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IntroductionEnhancing the generalization and reliability of speech recognition models in the field of air traffic control (ATC) is a challenging task. This is due to the limited storage, difficulty in acquisition, and high labeling costs of ATC speech data, which may result in data sample bias and class imbalance, leading to uncertainty and inaccuracy in speech recognition results. This study investigates a method for assessing the quality of ATC speech based on accents. Different combinations of data quality categories are selected according to the requirements of different model application scenarios to address the aforementioned issues effectively.MethodsThe impact of accents on the performance of speech recognition models is analyzed, and a fusion feature phoneme recognition model based on prior text information is constructed to identify phonemes of speech uttered by speakers. This model includes an audio encoding module, a prior text encoding module, a feature fusion module, and fully connected layers. The model takes speech and its corresponding prior text as input and outputs a predicted phoneme sequence of the speech. The model recognizes accented speech as phonemes that do not match the transcribed phoneme sequence of the actual speech text and quantitatively evaluates the accents in ATC communication by calculating the differences between the recognized phoneme sequence and the transcribed phoneme sequence of the actual speech text. Additionally, different levels of accents are input into different types of speech recognition models to analyze and compare the recognition accuracy of the models.ResultExperimental results show that, under the same experimental conditions, the highest impact of different levels of accents on speech recognition accuracy in ATC communication is 26.37%.DiscussionThis further demonstrates that accents affect the accuracy of speech recognition models in ATC communication and can be considered as one of the metrics for evaluating the quality of ATC speech.

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