Sam Sethi talked with Sam Liang about his entrepreneurial journey, from his beginnings as a computer student at Peking University through to a software engineer at Google where he was responsible for developing the blue dot on Google Maps.

Sam sold his first startup, a location platform called Alohar Mobile to Alibaba, before finally starting Otter, the 30-person startup team that hails from Google, Facebook, Nuance, Yahoo, as well as Stanford, Duke, MIT and Cambridge.

Otter was founded, just over three years ago and has raised $13 million in funding from the who’s who of Silicon Valley. e.g Horizons Ventures – a backer of Viv, DeepMind, Siri, Slack and others – who led the $10 million Series A. Also participating were Bridgewater Associates, i-Hatch Ventures, MetaLab, Jay Markley, and Boston investors Jim Pallotta and Stu Porter.

Seed investors included Tim Draper through Draper Associates and Draper Dragon; Dave Morin through Slow Ventures; David Cheriton "The billionaire Professor and 1st investor in Google"; SV Tech Ventures, Danhua Capital, and 500 Startups.

Otter wants to make it as easy to search your voice conversations as it is to search your email and texts. The idea to create a new voice assistant focused on transcribing everyday conversations – like meetings and interviews.

Essentially, a voice recorder that offers automatic transcription, Otter is designed to be able to understand and capture long-form conversations that take place between multiple people.

This is a different sort of voice technology than what’s been developed today for voice assistance – as with Alexa or Google Assistant.

The existing technologies are not good enough for human-to-human conversations,” explains Sam Liang.

“Google’s voice API has been trained to optimize voice search,” he says, adding that when people talk to voice assistants, it’s typically only one person talking and they tend to speak more slowly and clearly than usual.

They also often ask shorter questions, like “what’s the weather?,” not carry on long conversations.

“Human meetings are much more complicated, it usually involves at least two people, and the people could talk for an hour. It’s a long-form conversation.”

With Otter, the goal is to capture those conversations – meetings, interviews, lectures, etc. – and turn them into a searchable archive where everything said is immediately transcribed by Otter's software.

The entire technology stack, including speech recognition, was built in-house. The company is not using existing speech recognition APIs, because they wanted to improve the accuracy, and optimize for multiple speakers, says Liang.

To identify when someone else starts talking, Otter uses a technology called diarization to separate each individual speaker; it then generates a voice print for each person’s voice.

Broadly speaking, this is like the voice equivalent to facial recognition, with the voice print being used to identify the speaker going forward.

Sam envisions a number of potential use cases for Otter's technology, including in ente