In machine learning, you need to know what’s in your audio data. Discover how data augmentation with codec compressed audio can help your models generalize.
The impact of voice assistants on today’s youth is actually more helpful than you may think. Luckily, there are a plethora of fun, sound-based games.
Artificial Emotional Intelligence is a relatively new field, but one in which there is tremendous activity. Supported by a plethora of new open-source tools and machine learning frameworks, a programmer can now build a first prototype of an emotion-aware dialog machine in about two working days. But how good is the technology? And how can we develop better emotion AI?
This blog post is a roundup of voice emotion analytics companies. These are companies that can process an audio file containing human speech, extract the paralinguistic features and interpret these as human emotions, then provide an analysis report or other service based on this information.
- Nanocasting – Sean Gilligan, Sound Branch – Voice Tech Podcast ep.02117th February 2019 - 15:37
- Machine Learning Signals – Christopher Oates, audEERING – Voice Tech Podcast ep.0203rd February 2019 - 16:30
- Elevated Voice Design – Vasili Shynkarenka, Invocable – Voice Tech Podcast ep.01920th January 2019 - 15:53
- Prototypes & Personas – Kasia Ryniak & Rafal Cymerys, Upside – Voice Tech Podcast ep.0186th January 2019 - 17:22
- Constructing Multi-Turn Conversational Voice Experiences with a Knowledge Graph14th February 2019 - 23:15
- Summary of the Voicebot.ai 2019 predictions survey10th January 2019 - 01:12
- Improve your machine learning with compressed audio31st December 2018 - 17:54
- Why Voice Assistants are Beneficial to Children13th December 2018 - 12:10