Detecting Syllable-Level Pronunciation Stress with A Self-Attention Model

• Main Authors: Weiying, Wang, Akinori, Nakajima
• Format: Journal Article
• Language: English
• Published: 01-11-2023
• Subjects: Computer Science - Computation and Language; Computer Science - Sound
• DOI: 10.48550/arxiv.2311.00301

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.