Akinori Nakajima

Wang Weiying, Nakajima Akinori

One precondition of effective oral communication is that words should be pronounced clearly, especially for non-native speakers. Word stress is the key to clear and correct English, and misplacement of syllable stress may lead to misunderstandings. Thus, knowing the stress level is important for English speakers and learners. This paper presents a self-attention model to identify the stress level for each syllable of spoken English. Various prosodic and categorical features, including the pitch level, intensity, duration and type of the syllable and its nuclei (the vowel of the syllable), are explored. These features are input to the self-attention model, and syllable-level stresses are predicted. The simplest model yields an accuracy of over 88% and 93% on different datasets, while more advanced models provide higher accuracy. Our study suggests that the self-attention model can be promising in stress-level detection. These models could be applied to various scenarios, such as online meetings and English learning.

Vincent Corlay, Tatsuya Nakazato, Kanako Yamaguchi et al.

The advent of deep learning and recurrent neural networks revolutionized the field of time-series processing. Therefore, recent research on spectrum prediction has focused on the use of these tools. However, spectrum prediction, which involves forecasting wireless spectrum availability, is an older field where many "classical" tools were considered around the 2010s, such as Markov models. This work revisits high-order Markov models for spectrum prediction in dynamic wireless environments. We introduce a framework to address mismatches between sensing length and model order as well as state-space complexity arising with large order. Furthermore, we extend this Markov framework by enabling fine-tuning of the probability transition matrix through gradient-based supervised learning, offering a hybrid approach that bridges probabilistic modeling and modern machine learning. Simulations on real-world Wi-Fi traffic demonstrate the competitive performance of high-order Markov models compared to deep learning methods, particularly in scenarios with constrained datasets containing outliers.