Researchers to Develop Accent Detection AI

The team of scientists was from Cisco, the Moscow Institute of Physics and Technology and the Higher School of Economics
13 July 2018   1740

A team of scientists used machine learning to develop an improved model for speech recognition. This is reported by Venture Beat.

Previously, scientists manually identified phonological similarities between units of language in general American English and the pronunciation dictionary of the Carnegie Mellon University. To create an improved model, they went non-standard way and allowed it to automatically form the rules. Then, it compared the resulting unique list with a set of examples from George Mason University's speech accents archive.

More non-native accented speech data is necessary to enhance the performance of … existing [speech recognition] models. However, its synthesis is still an open problem.
 

Researchers

Based on the received examples, the team created a phonetic data set, through which a neural network, often used for speech recognition, was trained. The accuracy of the definition of words, after overcoming the mark of 800,000 examples, was 59%.

The study was called preliminary due to fewer sounds in the Carnegie-Mellon University dictionary. Despite phonetic coincidences in 13 out of 20 dictionary comparisons, scientists managed to increase the data array from 103 thousand phonetic transcriptions with one accent to 1 million samples with several accents.

BNC to Monitor BTC Community's Mood

The system called Twitter Sentiment analyzes over 34M BTC-related Twitter posts each week, using AI to track the mood of the community
22 January 2020   477

Blockchain-based New Zealand-based research firm Brave New Coin (BNC) has unveiled a new system for measuring the mood of the Bitcoin community based on Twitter messages.

According to BNC, the new Twitter Sentiment rating system analyzes over 34 million BTC-related Twitter posts each week. The company uses artificial intelligence (AI) algorithms that look for records containing the words bitcoin, $ BTC and BTC and others.

BNC notes that user sentiment continues to be a “significant” factor in the price and dynamics of digital assets, and a new technique has been developed to track these sentiments. According to the BNC, it took 18 months to launch the Bitcoin Twitter Sentiment. The data obtained is divided into seven categories - Opinion, Technical Information, Inside the Network, Advertising, Bots, Macros and Hacking.

For the week ending January 17, the most common entries were in the Opinion category - their number was 30.42% of all data received. In second place was the category Technical Information, and in third inside the network (includes information on mining and hashrate).

BNC spokeswoman Pierre Ansaldi said that during the first quarter of this year, the company will also launch community sentiment analysis tools for other crypto assets.