In this paper, a multimodal intelligent acoustic sensor is used for an in-depth study and analysis of English pronunciation signal acquisition and calibration analysis of English phonetic symbols based on the acquired sound signals. This paper proposes a bimodal fusion algorithm around the direction of feature extension and fusion of acoustic recognition features. After each unimodal classification error cost is minimized, the current fusion process is determined by adaptive weights to fix its one decision layer on the fusion. The adaptive weight approach in this algorithm improves the drawback of always identifying one mode as the optimal mode in fixed-weight fusion and further improves applicability and performance compared to unimodal recognition. The random network generation algorithm is used to generate a random network for sound source data acquisition; then, the algorithm is investigated using the decomposition containing fusion center algorithm to each node, and data preprocessing is implemented at each node; finally, the distributed consistency algorithm based on average weights is used for consistent averaging iterations to achieve a consistent speech enhancement effect at each node. The experimental results show that this distributed algorithm can effectively suppress the interference of noncoherent noise, and each node can obtain an enhanced signal close to the source signal-to-noise ratio. In this study, factors that may affect the readability of spoken texts are summarized, analyzed, defined, and extracted, and the difficulty of spoken items obtained from the divisional scoring model is used as the dependent variable, and the extracted influencing factors are used as independent variables for feature screening, model construction, and tuning, and the generated results are interpreted and analyzed. From this, it was found that phonological features have a strong influence on the readability of spoken texts, mainly in features such as phonemes, syllables, and stress. This study is summarized, and the shortcomings of location-based contextual mobile learning of spoken English in terms of student management, device deployment, and empirical evidence are pointed out, to provide references and lessons for the research on IT-supported language learning.

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