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.