GN-GloVe AI to Have No Gender Prejudice

New model of scientists at the University of California showed 35% fewer mistakes
10 September 2018   265

Scientists from the University of California created a model for learning neural networks called Gender-Neutral Global Vectors (GN-GloVe). The development is intended for AI, specializing in the recognition of speech and texts. According to programmers, this training model will reduce the percentage of false gender associations. This is reported by Venture Beat.

Neural networks designed for speech recognition are trained on special data sets. However, these kits carry the imprint of a living language, filled with stereotypes. For example, the words "cook" or "secretary" are more often associated with the female sex, and "locksmith" or "welder" - with the male. Or, another examples: "doctor" is usually replaced by the pronoun "he", and "nurse" - "she".

Artificial intelligence, trained on such datasets, assimilates all the prejudices inherent in them. In particular, if a "doctor" is mentioned in the text without mentioning a particular sex, the neural network will more likely be considered a man. GN-GloVe, as claimed by its creators, removes the false associations with the sex.

This technology does not affect those areas where the sex is specified directly. To achieve this effect, the method determines gender-neutral words simultaneously with the formation of the semantic vectors of the text. Another advantage of development scientists call independence from the language being processed.

In a comparative analysis with GloVe, one of the most common teaching methods, the new model of scientists at the University of California showed 35% fewer mistakes due to false identification of a person's sex by type of activity.

Data sets for training contain many prerequisites for the formation of retraining errors. For example, smart speakers from Amazon and Google are 30% less likely to recognize English, pronounced with accent. And this problem is not just about speech: face recognition algorithms are worse at copying images of African Americans than Caucasians.

The bias of artificial intelligence bias surfaced in the work of Princeton University scientists in early 2017. While protection from such errors does not exist, however, similar GN-GloVe algorithms can in time reduce the bias error to an acceptable level.

Microsoft to Use AI to Create Human Voice

Synthetic voice is nearly indistinguishable from recordings of people
27 September 2018   457

Researchers from Microsoft recorded computer voice, imitating human speech. To overcome the difficulties of the traditional model, they used neural networks for speech synthesis. Microsoft promises to provide support for 49 languages ​​and the ability to create unique voices for the needs of companies in the near future.

Synthesis of speech with the help of neural networks involves comparing the stress and length (so-called prosody) of the speaker's speech units, as well as their synthesis into a computer voice. In systems of traditional speech synthesis, prosody is divided into acoustic and linguistic analysis, controlled by various models. As a result, the speech is noisy and indistinct. Representatives of Microsoft argue that in the model of neural synthesis two stages are combined into one, so the voice sounds like a real one.

The developers are convinced that the synthesis of speech with the help of neural networks will make it more natural to communicate with virtual interlocutors and assistants. Moreover, it will enable you to convert e-books into audiobooks and will allow you to change the scoring of built-in navigators.

Microsoft Neural TTS
Microsoft Neural TTS

Azure computing power is available for real-time use, and Azure Kubernetes is responsible for this. Simultaneous application of neural synthesis of speech together with traditional speaks about expansion and increase of availability of service. At the moment, there are a female voice named Jessa and a man named Guy.

Microsoft is competing in speech recognition and synthesis technologies with Google, which updated its services in late August 2018. Google Cloud announced the release of a stable API for the synthesis of speech Cloud Text-to-Speech with the experimental function of audio profiles and support for several new languages.