Lately, we have been developing our own Finnish Speech to Text model and now it’s ready! Finnish Speech to Text model is designed to help large companies detect business-critical patterns from existing phone calls, such as sales leads, quality leaks, unhappy and leaving customers. Analysing phone calls helps sales managers to find new sales leads (upsell and win-back leads) from existing conversations in near real-time while also providing customer service managers with a tool to help improve customer service quality.
Converting customer phone calls into text is a necessary step to find business-critical patterns. After that, all calls become searchable and AI-based models automatically detect each call’s sentiment, topic, etc. This step spawns actionability which could mean contacting clients, improving services, taking advantage of sales opportunities, etc. Feelingstream’s value creation process is visualised in the following graph:
There are a couple of reasons why we developed our own Speech to Text model instead of using cloud-based services:
- Higher accuracy. Our Speech to Text (speech-to-text or ASR - Automatic Speech Recognition) model is more accurate because it is focused on customer conversations in Finnish in specific sectors. This was kept in mind while collecting training data for the model.
- Adaptability. Our Speech to Text model is easily adaptable to customer-specific words. For example, if the model doesn’t recognize specific product names, the model could easily be fine-tuned to increase accuracy.
- On-premises. Our Speech to Text model can be used on-premises without any external connections.
Adopting the Finnish speech-to-text model
The model in the Finnish language was built based on data collected from public sources. The crowdsourcing is great to run this project. First tests on real customer phone calls have been very positive. Even if the model has not been adapted to specific customer’s phone calls, it has a lower word error rate than Google’s Speech to Text API. Feelingstream’s FIN ASR model has the added advantage that its error rate could be decreased even further.
If you are interested in detecting business-critical patterns in customer conversations, contact us or come see us at AI Monday in Helsinki on 21st October 2019. Please see the recording in English (the article is in Finnish, but the video is in English).
Risto Hinno is a Lead Feeling Scientist at Feelingstream and takes care of developing AI-based models for detecting business-critical patterns from customer conversations.