320- Dr. Trish Scanlon on How Voice Technologies Should Work For Kids

<p>Today Tom is sitting down with Dr. Trish Scanlon, CEO and founder of SoapBox Labs. Dr. Scanlon is one of the foremost leaders in voice technology <span style="color: #0f1419;">— especially with regards to children’s voices. She has over 20 years of experience working in speech recognition technology, including Bell Laboratories and IBM. In 2018, Dr. Scanlon was named one of</span> <span style="color: #0f1419;"><em>Forbes</em></span><span style= "color: #0f1419;">’ top 50 “Women in Tech.” In 2020, she was ranked sixth of seventeen global visionaries in voice by industry-leading publication,</span> <span style= "color: #0f1419;"><em>Voicebot.ai.</em></span></p> <p><span style="color: #0f1419;">Listen in as Tom and Dr. Scanlon discuss the possibilities of voice technologies, why children pose a unique challenge in the field, and how tech can be keeping ethics and well-being at the center. Don’t miss out on this A.I. deep-dive on learning, voice tech, and creating more access for learners with differences.</span></p> <p> </p> <p><strong>Key Takeaways:</strong></p> <p>[:08] About today’s episode.</p> <p>[:54] Tom welcomes Dr. Scanlon to the podcast.</p> <p>[1:12] How did Dr. Scanlon become interested in voice technology?</p> <p>[2:32] Was Dr. Scanlon able to find advisors 20 years ago in voice technology?</p> <p>[3:50] Which machine learning tools are most useful in speech recognition?</p> <p>[5:24] When did Dr. Scanlon recognize that speech recognition was particularly challenging for children?</p> <p>[8:20] The origin story of Dr. Scanlon’s company, SoapBox Labs.</p> <p>[10:35] Dr. Scanlon explains the service that SoapBox Labs offers its clients.</p> <p>[12:37] About the Turnaround for Children Toolbox.</p> <p>[13:17] The current and planned applications for voice recognition, speech therapy, and voice tech in learning and development in the classroom.</p> <p>[15:53] How they manage varying accents in speech technology.</p> <p>[19:06] Discussing the challenges around the biases in voice recognition technology.</