Searching online returns quite a few results, some of which are quite tailored to your needs:
Tepperman, J., & Narayanan, S. (2005, March). Automatic syllable stress detection using prosodic features for pronunciation evaluation of language learners. In 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) Proceedings (Vol. 1, pp. I-937). IEEE.
This paper presents a new technique for automatic syllable stress detection that is tailored for language-learning purposes. Our method, which uses basic prosodic features and others related to the fundamental frequency slope and RMS energy range, is at least as accurate as an expert human listener, but requires no human supervision other than a pre-defined dictionary of expected lexical stress patterns for all words in the system’s vocabulary. Optimal feature choices exhibited an 87-89% accuracy compared with human-tagged stress labels, exceeding the inter-human agreement commonly held to be about 80%. (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.130.9566&rep=rep1&type=pdf)
Imoto, K., Tsubota, Y., Raux, A., Kawahara, T., & Dantsuji, M. (2002). Modeling and automatic detection of English sentence stress for computer-assisted English prosody learning system. In Seventh International Conference on Spoken Language Processing. (This seems closest to what you need)
We address sentence-level stress detection of English for Computer-Assisted Language Learning (CALL) by Japanese students. Stress models are set up by considering syllable structure and position of the syllable in a phrase, which will provide diagnostic information for students. We also propose a two-stage recognition method that first detects the presence of stress and then identifies the stress level using different weighted combinations of acoustic features. The modeling is coherent with conventional linguistic observations. The method achieves stress recognition rate of 95.1% for native and 84.1% for Japanese speakers. (http://www.cs.cmu.edu/~antoine/papers/icslp2002b.pdf)
Ananthakrishnan, S., & Narayanan, S. S. (2005, March). An automatic prosody recognizer using a coupled multi-stream acoustic model and a syntactic-prosodic language model. In 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) Proceedings (Vol. 1, pp. I-937). IEEE.
In this paper, we build a prosody recognition system that detects stress and prosodic boundaries at the word and syllable level in American English using a coupled Hidden Markov Model (CHMM) to model multiple, asynchronous acoustic feature streams and a syntactic-prosodic model that captures the relationship between the syntax of the utterance and its prosodic structure. Experiments show that the recognizer achieves about 75% agreement on stress labeling and 88% agreement on boundary labeling at the syllable level. (https://sail.usc.edu/publications/files/ananthakrishnanicassp2005.pdf)