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Curricular analytics (CA) is a quantitative method that analyzes the sequence of courses (curriculum) that students in an undergraduate academic program must complete to fulfill the requirements of the program. The main hypothesis of CA is that the less complex a curriculum is, the more likely it is that students complete the program. This study compares the curricular complexity of undergraduate physics programs at 60 institutions in the United States. The institutions were divided into three tiers based on national rankings of the physics graduate program, and the means of each tier were compared. No significant difference between the means of each tier was found, indicating that there is not a relationship between program curricular complexity and program ranking. Further analysis focused on the physics, chemistry, and mathematics courses, defined as the core courses of the curriculum. Significant differences in the number of required core courses and the complexity per core course were measured between the tiers; both were measured as large effects. Programs with the highest rankings required fewer core courses while having a higher complexity per core course. These institutions have more strict prerequisite requirements than lower ranking programs. This study also showed complexity was quantitatively related to curricular flexibility operationalized as the number of available eight-semester degree plans. The number of available degree plans exponentially decreased with increasing core complexity per course. Modifications to a curriculum at one institution were analyzed; a similar relationship between the number of available degree plans and increasing complexity per core course was found. Published by the American Physical Society2024 

In recent decades, considerable research has been devoted to speech enhancement leveraging the short-term Fourier transform (STFT) analysis. As speech processing technology evolves, the significance of phase information in enhancing speech intelligibility becomes more noticeable. Typically, the Hanning window has been widely employed as analysis window in STFT. In this study, we propose the Chebyshev window for phase analysis, and the Hanning window for magnitude analysis. Next, we introduce a novel cepstral domain enhancement approach designed to robustly reinforce the harmonic structure of speech. The performance of our model is evaluated using the DNS challenge test set as well as the naturalistic APOLLO Fearless Steps evaluation set. Experimental results demonstrate that the Chebyshev-based phase solution outperforms the Hanning option for in phase-aware speech enhancement. Furthermore, the incorporation of quefrency emphasis proves effective in enhancing overall speech quality. 

Machine learning models were constructed to predict student performance in an introductory mechanics class at a large land-grant university in the United States using data from 2061 students. Students were classified as either being at risk of failing the course (earning a D or F) or not at risk (earning an A, B, or C). The models focused on variables available in the first few weeks of the class which could potentially allow for early interventions to help at-risk students. Multiple types of variables were used in the model: in-class variables (average homework and clicker quiz scores), institutional variables [college grade point average (GPA)], and noncognitive variables (self-efficacy). The substantial imbalance between the pass and fail rates of the course, with only about 10% of students failing, required modification to the machine learning algorithms. Decision threshold tuning and upsampling were successful in improving performance for at-risk students. Logistic regression combined with a decision threshold tuned to maximize balanced accuracy yielded the strongest classifier, with a DF accuracy of 83% and an ABC accuracy of 81%. Measures of variable importance involving changes in balanced accuracy identified homework grades, clicker grades, college GPA, and the fraction of college classes successfully completed as the most important variables in predicting success in introductory physics. Noncognitive variables added little predictive power to the models. Classification models with performance near the best-performing models using the full set of variables could be constructed with very few variables (homework average, clicker scores, and college GPA) using straightforward to implement algorithms, suggesting the application of these technologies may be fairly easy to include in many physics classes. Published by the American Physical Society2024 

The Fearless Steps Apollo (FS-APOLLO) resource is a collection of 150,000 hours of audio, associated meta-data, and supplemental speech technology infrastructure intended to benefit the (i) speech processing technology, (ii) communication science, team-based psychology, and (iii) education/STEM, history/preservation/archival communities. The FS-APOLLO initiative which started in 2014 has since resulted in the preservation of over 75,000 hours of NASA Apollo Missions audio. Systems created for this audio collection have led to the emergence of several new Speech and Language Technologies (SLT). This paper seeks to provide an overview of the latest advancements in the FS-Apollo effort and explore upcoming strategies in big-data deployment, outreach, and novel avenues of K-12 and STEM education facilitated through this resource. 

Speaker tracking in spontaneous naturalistic data continues to be a major research challenge, especially for short turn-taking communications. The NASA Apollo-11 space mission brought astronauts to the moon and back, where team based voice communications were captured. Building robust speaker classification models for this corpus has significant challenges due to variability of speaker turns, imbalanced speaker classes, and time-varying background noise/distortions. This study proposes a novel approach for speaker classification and tracking, utilizing a graph attention network framework that builds upon pretrained speaker embeddings. The model’s robustness is evaluated on a number of speakers (10-140), achieving classification accuracy of 90.78% for 10 speakers, and 79.86% for 140 speakers. Furthermore, a secondary investigation focused on tracking speakers-of-interest(SoI) during mission critical phases, essentially serves as a lasting tribute to the 'Heroes Behind the Heroes'. 

This study is focused on understanding and quantifying the change in phoneme and prosody information encoded in the Self-Supervised Learning (SSL) model, brought by an accent identification (AID) fine-tuning task. This problem is addressed based on model probing. Specifically, we conduct a systematic layer-wise analysis of the representations of the Transformer layers on a phoneme correlation task, and a novel word-level prosody prediction task. We compare the probing performance of the pre-trained and fine-tuned SSL models. Results show that the AID fine-tuning task steers the top 2 layers to learn richer phoneme and prosody representation. These changes share some similarities with the effects of fine-tuning with an Automatic Speech Recognition task. In addition, we observe strong accent-specific phoneme representations in layer 9. To sum up, this study provides insights into the understanding of SSL features and their interactions with fine-tuning tasks. 

Automatic pronunciation assessment (APA) plays an important role in providing feedback for self-directed language learners in computer-assisted pronunciation training (CAPT). Several mispronunciation detection and diagnosis (MDD) systems have achieved promising performance based on end-to-end phoneme recognition. However, assessing the intelligibility of second language (L2) remains a challenging problem. One issue is the lack of large-scale labeled speech data from non-native speakers. Additionally, relying only on one aspect (e.g., accuracy) at a phonetic level may not provide a sufficient assessment of pronunciation quality and L2 intelligibility. It is possible to leverage segmental/phonetic-level features such as goodness of pronunciation (GOP), however, feature granularity may cause a discrepancy in prosodic-level (suprasegmental) pronunciation assessment. In this study, Wav2vec 2.0-based MDD and Goodness Of Pronunciation feature-based Transformer are employed to characterize L2 intelligibility. Here, an L2 speech dataset, with human-annotated prosodic (suprasegmental) labels, is used for multi-granular and multi-aspect pronunciation assessment and identification of factors important for intelligibility in L2 English speech. The study provides a transformative comparative assessment of automated pronunciation scores versus the relationship between suprasegmental features and listener perceptions, which taken collectively can help support the development of instantaneous assessment tools and solutions for L2 learners. 

Apollo 11 was the first manned space mission to successfully bring astronauts to the Moon and return them safely. As part of NASA’s goal in assessing team and mission success, all voice communications within mission control, astronauts, and support staff were captured using a multichannel analog system, which until recently had never been made available. More than 400 personnel served as mission specialists/support who communicated across 30 audio loops, resulting in 9,000+ h of data. It is essential to identify each speaker’s role during Apollo and analyze group communication to achieve a common goal. Manual annotation is costly, so this makes it necessary to determine robust speaker identification and tracking methods. In this study, a subset of 100hr derived from the collective 9,000hr of the Fearless Steps (FSteps) Apollo 11 audio data were investigated, corresponding to three critical mission phases: liftoff, lunar landing, and lunar walk. A speaker recognition assessment is performed on 140 speakers from a collective set of 183 NASA mission specialists who participated, based on sufficient training data obtained from 5 (out of 30) mission channels. We observe that SincNet performs the best in terms of accuracy and F score achieving 78.6% accuracy. Speaker models trained on specific phases are also compared with each other to determine if stress, g-force/atmospheric pressure, acoustic environments, etc., impact the robustness of the models. Higher performance was obtained using i-vector and x-vector systems for phases with limited data, such as liftoff and lunar walk. When provided with a sufficient amount of data (lunar landing phase), SincNet was shown to perform the best. This represents one of the first investigations on speaker recognition for massively large team-based communications involving naturalistic communication data. In addition, we use the concept of “Where’s Waldo?” to identify key speakers of interest (SOIs) and track them over the complete FSteps audio corpus. This additional task provides an opportunity for the research community to transition the FSteps collection as an educational resource while also serving as a tribute to the “heroes behind the heroes of Apollo.”