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Spinvox, a voice-recognition startup based in London, has landed a deal with Spanish mobile operator Telefonica to provide the mobile giant with voice-to-text technology. The agreement cover’s Telefonica’s operations in Europe and Latin America, which total some 100 million subscribers, and is reportedly worth $100 million.
The news is a major boost to the speech-recognition industry, which has suffered from quality concerns and a lack of sustainable business models.
Spinvox has cleverly tapped into a major painpoint for the mobile operators: As Apple, Nokia and others increasingly offer services that do not rely on the mobile operators’ networks and subscriber data, carriers are scrambling to develop popular services that do.
Carriers hope that the Spinvox service will keep their subscribers using voice services — an essential but declining market for carriers. The thinking here is that making deals directly with mobile operators will mean that Spinvox will only work with mobile operators’ voice services, rather than those offered by pesky VoIP providers like Skype.
“It’s essential that we keep voice as part of…improving our communications and making it seamless,” said Daniel Doulton, a co-founder of Spinvox who was speaking at this week’s HiT innovation event in Barcelona.
To date, the main service that Spinvox has offered is a service to turn voicemail messages into text messages. It’s been doing this already for Vodafone Spain and fixed providers like Cincinnati Bell.
Now the company has released its APIs and is riding the wave of mobile apps, integrating with social networking services like Twitter — an opportunity that Doulton says could be worth some $80 billion. I can see how the idea of mobile, voice-based tweeting, or using an application to update a client log in Salesforce, could be appealing, especially since so many other apps that attempted to do this have proven unreliable.
Doulton said that Spinvox also has longer-term aspirations to offer voice-based search services as well. Using YouTube as an example of “awful” indexing, he described a scenario where the content of the videos could be mined for different words, which could then be converted into text or voice tags to find the material.
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