The Trouble with Tinker: An Examination of Student Free Speech Rights in the Digital Age

The boundaries of the schoolyard were once clearly delineated by the physical grounds of the school. In those days, it was relatively easy to determine what sort of student behavior fell within an educator’s purview, and what lay beyond the school’s control. Technological developments have all but erased these confines and extended the boundaries of the school environment somewhat infinitely, as the internet and social media allow students to interact seemingly everywhere and at all times. As these physical boundaries of the schoolyard have disappeared, so too has the certainty with which an educator might supervise a student’s behavior. Because smartphones, tablets, and computers abound, the ways in which students are able to communicate have changed dramatically in the new millennium, but the law governing the free speech rights of students in American public schools has not kept pace. Current law allows educators to punish student speakers when their in-school speech disrupts the school environment, or is likely to do so—but it is not clear that this same standard should apply to student speech that is posted online away from school, or whether a school should be able to punish off-campus online student speech at all. Because the Supreme Court of the United States has not yet spoken on the issue, and in the absence of a better standard, the courts that have addressed the issue of problematic off-campus online student speech have applied this standard that bases a school’s ability to punish the speaker on the (potential) disruptiveness of his or her speech. This Note explores that which the First Amendment guarantees to adult citizens and the ways in which these guarantees differ for public school students in school, as governed by four major Supreme Court decisions in the past fifty years. This Note then examines the recent cases in which courts have applied this precedent to off-campus online student speech for which the speakers were punished by their schools, and analyzes the ways in which the application of the same standard in these cases has led to drastically different outcomes. Ultimately, this Note contends that educators must be able to supervise student online activities to some extent, and proposes a new standard by which a public school would be able to punish a student for his or her off-campus online speech only if that speech was actually of concern to the school, and if that speech interfered with the rights of others in the school community

BridgeNets: Student-Teacher Transfer Learning Based on Recursive Neural Networks and its Application to Distant Speech Recognition

Despite the remarkable progress achieved on automatic speech recognition, recognizing far-field speeches mixed with various noise sources is still a challenging task. In this paper, we introduce novel student-teacher transfer learning, BridgeNet which can provide a solution to improve distant speech recognition. There are two key features in BridgeNet. First, BridgeNet extends traditional student-teacher frameworks by providing multiple hints from a teacher network. Hints are not limited to the soft labels from a teacher network. Teacher's intermediate feature representations can better guide a student network to learn how to denoise or dereverberate noisy input. Second, the proposed recursive architecture in the BridgeNet can iteratively improve denoising and recognition performance. The experimental results of BridgeNet showed significant improvements in tackling the distant speech recognition problem, where it achieved up to 13.24% relative WER reductions on AMI corpus compared to a baseline neural network without teacher's hints.Comment: Accepted to 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018

SNR-Based Teachers-Student Technique for Speech Enhancement

It is very challenging for speech enhancement methods to achieves robust performance under both high signal-to-noise ratio (SNR) and low SNR simultaneously. In this paper, we propose a method that integrates an SNR-based teachers-student technique and time-domain U-Net to deal with this problem. Specifically, this method consists of multiple teacher models and a student model. We first train the teacher models under multiple small-range SNRs that do not coincide with each other so that they can perform speech enhancement well within the specific SNR range. Then, we choose different teacher models to supervise the training of the student model according to the SNR of the training data. Eventually, the student model can perform speech enhancement under both high SNR and low SNR. To evaluate the proposed method, we constructed a dataset with an SNR ranging from -20dB to 20dB based on the public dataset. We experimentally analyzed the effectiveness of the SNR-based teachers-student technique and compared the proposed method with several state-of-the-art methods.Comment: Published in 2020 IEEE International Conference on Multimedia and Expo (ICME 2020

Living, Learning, and Leading at Linfield College

Kelsey Bruce discusses student engagement at Linfield College with regard to leadership through student/faculty collaborative research with Dr. Megan Bestwick, speech and debate, and the Linfield Residence Life team.https://digitalcommons.linfield.edu/inauguration2019_students/1003/thumbnail.jp

Student Service to the High School Forensics Community: Insights Gained from Hosting the Annual Singletary Speech and Debate Tournament

Kelsey Bruce discusses student engagement at Linfield College with regard to hosting the annual Singletary Speech and Debate Tournament.https://digitalcommons.linfield.edu/inauguration2019_students/1002/thumbnail.jp

Dialing It Back: Why Courts Should Rethink Students’ Privacy and Speech Rights as Cell Phone Communications Erode the ‘Schoolhouse Gate’

The ubiquity of cell phones in today’s society has forced courts to change or dismiss established, but inapplicable analytical frameworks. Two such frameworks in the school setting are regulations of student speech and of student searches. This Article traces the constitutional jurisprudence of both First Amendment off-campus speech protection and Fourth Amendment search standards as applied to the school setting. It then analyzes how the Supreme Court’s ruling in Riley v. California complicates both areas. Finally, it proposes a pragmatic solution: by recognizing a categorical First Amendment exception for “substantial threats” against the school community, courts could accommodate students’ constitutional rights while upholding school administrators’ ability to maintain a safe environment

Improved Noisy Student Training for Automatic Speech Recognition

Recently, a semi-supervised learning method known as "noisy student training" has been shown to improve image classification performance of deep networks significantly. Noisy student training is an iterative self-training method that leverages augmentation to improve network performance. In this work, we adapt and improve noisy student training for automatic speech recognition, employing (adaptive) SpecAugment as the augmentation method. We find effective methods to filter, balance and augment the data generated in between self-training iterations. By doing so, we are able to obtain word error rates (WERs) 4.2%/8.6% on the clean/noisy LibriSpeech test sets by only using the clean 100h subset of LibriSpeech as the supervised set and the rest (860h) as the unlabeled set. Furthermore, we are able to achieve WERs 1.7%/3.4% on the clean/noisy LibriSpeech test sets by using the unlab-60k subset of LibriLight as the unlabeled set for LibriSpeech 960h. We are thus able to improve upon the previous state-of-the-art clean/noisy test WERs achieved on LibriSpeech 100h (4.74%/12.20%) and LibriSpeech (1.9%/4.1%).Comment: 5 pages, 5 figures, 4 tables; v2: minor revisions, reference adde

The University of Alaska, Juneau Campus Newspaper

UAJ testifies before subcommittee -- Thar she blows -- Student government update -- Channels -- Speech and communications program expands -- Counselor's corner -- UAJ announces fall graduates -- Careers day held at Bill Ray Center -- Sports & Activities -- Second month entertainments -- Baleen cuisine -- Whale's tail classifie