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Privacy is a hot topic for policymakers across the globe, including the United States. Evolving advances in AI and emerging concerns about the misuse of personal data have pushed policymakers to draft legislation on trustworthy AI and privacy protection for its citizens. This paper presents the state of the privacy legislation at the U.S. Congress and outlines how voice data is considered as part of the legislation definition. This paper also reviews additional privacy protection for children. This paper presents a holistic review of enacted and proposed privacy laws, and consideration for voice data, including guidelines for processing children’s data, in those laws across the fifty U.S. states. As a groundbreaking alternative to actual human data, ethically generated synthetic data allows much flexibility to keep AI innovation in progress. Given the consideration of synthetic data in AI legislation by policymakers to be relatively new, as compared to that of privacy laws, this paper reviews regulatory considerations for synthetic data. 

Bilingual children at a young age can benefit from exposure to dual language, impacting their language and literacy development. Speech technology can aid in developing tools to accurately quantify children’s exposure to multiple languages, thereby helping parents, teachers, and early-childhood practitioners to better support bilingual children. This study lays the foundation towards this goal using the Hoff corpus containing naturalistic adult-child bilingual interactions collected at child ages 2½, 3, and 3½ years. Exploiting self-supervised learning features from XLSR-53 and HuBERT, we jointly predict the language (English/Spanish) and speaker (adult/child) in each utterance using a multi-task learning approach. Our experiments indicate that a trainable linear combination of embeddings across all Transformer layers of the SSL models is a stronger indicator for both tasks with more benefit to speaker classification. However, language classification for children remains challenging. 

Although non-profit commercial products such as LENA can provide valuable feedback to parents and early childhood educators about their children’s or student’s daily communication interactions, their cost and technology requirements put them out of reach of many families who could benefit. Over the last two decades, smartphones have become commonly used in most households irrespective of their socio-economic background. In this study, conducted during the COVID-19 pandemic, we aim to compare audio collected on LENA recorders versus smartphones available to families in an unsupervised data collection protocol. Approximately 10 hours of audio evaluated in this study was collected by three families in their homes during parent-child science book reading activities with their children. We report comparisons and found similar performance between the two audio capture devices based on their speech signal-tonoise ratio (NIST STNR) and word-error-rates calculated using automatic speech recognition (ASR) engines. Finally, we discuss implications of this study for expanding this technology to more diverse populations, limitations and future directions. 

Monitoring child development in terms of speech/language skills has a long-term impact on their overall growth. As student diversity continues to expand in US classrooms, there is a growing need to benchmark social-communication engagement, both from a teacher-student perspective, as well as student-student content. Given various challenges with direct observation, deploying speech technology will assist in extracting meaningful information for teachers. These will help teachers to identify and respond to students in need, immediately impacting their early learning and interest. This study takes a deep dive into exploring various hybrid ASR solutions for low-resource spontaneous preschool (3-5yrs) children (with & without developmental delays) speech, being involved in various activities, and interacting with teachers and peers in naturalistic classrooms. Various out-of-domain corpora over a wide and limited age range, both scripted and spontaneous were considered. Acoustic models based on factorized TDNNs infused with Attention, and both N-gram and RNN language models were considered. Results indicate that young children have significantly different/ developing articulation skills as compared to older children. Out-of-domain transcripts of interactions between young children and adults however enhance language model performance. Overall transcription of such data, including various non-linguistic markers, poses additional challenges.