In an age where pre-trained Machine Learning models are hot and readily available, it is easy to fall for the myth that you can use text-based models for speech analytics. Not the case.
While out-of-the-box models rely on text-based conventions, speech analytics platforms have been trained on data derived from the spoken conversation and tuned for the unstructured nature of voice communication. In addition, a wide range of acoustics are in play to provide contextual accuracy.
Join Vice President of AI Rick Britt and Data Scientist Kirsten Stallings as they dispel the myth that out-of-the-box text analytics works the same on speech data.
Join us to learn in this webinar:
- Scientific differences between transcribed spoken communication and text
- Head-to-head performance comparisons of models trained on spoken communication versus text data
- Natural Language Process (NLP) tools for overcoming common challenges in speech processing and modeling
- Machine Learning techniques for supervised vs unsupervised spoken communication
- Use cases for speech analytics solutions