Dialpad to Raise $50m to Expand Conversational AI

Funds are planed to be spent on strengthen and expand its AI services to additional products, including enterprise video chat service UberConference
17 July 2018   329

Tech company Dialpad reported on the closure of a $50 million funding round. Funds are planed to be spent on strengthen and expand its conversational AI services to additional products, including enterprise video chat service UberConference. This is reported by VentureBeat.

Dialpad uses VoiceAI from acquired TalkIQ into UberConference, Dialpad phone replacement, and offerings for call center customer service agents. VoiceAI can:

  • provide coaching tips
  • determine whether the person on the other end of a phone or video call is happy with what they’re hearing
  • automatically generate action items from meetings and speech-to-text transcripts.

Evolving from a product standpoint, we’ll be adding multiparty video to it [UberConference] shortly. Beyond that, we will be adding the same AI pieces to it that are in Dialpad and Dialpad Call Center. Having a unified artificial intelligence experience lets a business have much better visibility into how the business is being run, how they’re talking about their products, how their sales and support reps are providing support.
 

Craig Walker

CEO, Dialpad

Earlier Walker founded GrandCentral, a VoIP company acquired by Google in 2007 that would become Google Voice.

Iconiq Capital, Andreessen Horowitz, Amasia, Scale Ventures, and Section 32 took part in the investment. It follows a $17 million round last September. Since its launch in 2011, Dialpad has raised $120 million.

Funding will also be used to grow the company’s headcount by 100 employees. At the moment, it has 275 employees. 

Google to Create Accurate Online Speaker Diarization Tool

The development is based on a recurrent neural network
14 November 2018   28

Google reported on the creation of an innovative diarization algorithm - dividing the incoming audio stream into homogeneous segments in accordance with the belonging of words to a particular person. The company claims that the technology created is more efficient than previously known.

The development is based on a recurrent neural network (RNN). This architecture allows the use of internal memory for processing sequences of arbitrary length and is well suited for working with split audio. In the development of Google for each speaker stands out a separate copy of the RNN, isolating the statements.

Google experts note that their algorithm is completely transparent and controllable, which allows you to adjust the processing of the audio stream.

The developers tested the effectiveness of the new diarization algorithm using the NIST SRE 2000 CALLHOME test. The determination error was 7.6%. The previously used methods of clustering and selection using a neural network showed an error of 8.8% and 9.9%, respectively. In addition to fewer errors, the algorithm has sufficient performance to process the stream in real time.

The definition of replica ownership is an important component of the speech recognition system. Correct diarization allows to adapt better to the peculiarities of pronunciation and accent and to qualitatively separate the statements of different people. The technology will be used, in particular, in creating subtitles for video recordings. Properly recognized speech is easier to translate into other languages, which, for example, would be useful for online training courses. And the ability to process sound in real time will allow you to do it even live.