I plopped into the front seat, expecting to laugh in the face of the machine attempting to measure my age, gender, emotional state, and comfort level all through infrared cameras and other sensors. But sitting expectantly in the car, equipped with French automotive software company Valeo's Smart Cocoon 4.0 system, I was flabbergasted when it pinpointed my exact age. Getting that number right made me trust the car's biometric system more than I probably should have, even as tools that measure your heartbeat, track your eyes, head position, voice, and more enter vehicles everywhere. At CES this year, driver and passenger monitoring kept popping up. It's a preview of what will become commonplace in the driver's seat in the coming years.
Chinese startup iFlyTek boasts it has created for law enforcement AI technology that leverages voice biometrics to identify a person, writes Nikkei Asian Review. In upcoming years, iFlyTek aims to use it in fighting phone scams after rolling out the voiceprint recognition system across the country. "Because recordings are important evidence when it comes to phone scams, demand for voice recognition is growing," said Fu Zhonghua, the deputy head of iFlyTek's research center. Fu further states that the technology is aimed to be used in law enforcement and phone monitoring to identify scammers' voiceprints and hang up, but it can also be successfully implemented in finance. Government-owned China Construction Bank is already using voiceprints to verify customer identity alongside passwords.
Chinese artificial intelligence startup iFlytek says it has developed AI-powered technology that can accurately identify a person by his or her voice, for use in law enforcement. The company expects to be able to roll out a voiceprint recognition system nationwide in two to three years, said Fu Zhonghua, the deputy head of iFlytek's research center here. The Chinese market for such technology has the prospect of becoming a driver of earnings growth for iFlytek, which has been hit with U.S. sanctions for its alleged role in China's internationally criticized treatment of Muslim minorities. "Because recordings are important evidence when it comes to phone scams, demand for voice recognition is growing," Fu told reporters at the lab. The voiceprint recognition tool harnesses iFlytek's strength in using AI to analyze data.
One of the benefits of investing in a new flagship handset such as the Pixel 4, is that it gives you access to exclusive apps and features. Just as the likes of Samsung and OnePlus do, Google gives owners of its latest Pixel devices new toys to play with, and with the Pixel 4 this included the Recorder app. More than just a simple audio recording tool, Recorder also uses AI and voice recognition to automatically transcribe and label recordings in real time to make them far more useful. Now there's good news for anyone packing a Pixel 2, Pixel 3 or Pixel 3a: the Recorder app is no longer exclusive to the Pixel 4. The automatic transcription offered by the Recorder app is great for taking minutes of meetings, dictating documents and much more. The app is now being opened up to a wider range of users, giving many more people the chance to avoid the laborious task of transcribing recordings by hand.
Select the conversation in which you want to tag speakers. Otter will automatically tag speakers who have previously been identified. For new speakers, please teach Otter their voice by identifying them in the conversation. Select the unknown speaker icon to start identifying the speaker. Otter will list recent speakers for you to choose.
We present a scoring approach for speaker verification that mimics the standard PLDA-based backend process used in most current speaker verification systems. However, unlike the standard backends, all parameters of the model are jointly trained to optimize the binary cross-entropy for the speaker verification task. We further integrate the calibration stage inside the model, making the parameters of this stage depend on metadata vectors that represent the conditions of the signals. We show that the proposed backend has excellent out-of-the-box calibration performance on most of our test sets, making it an ideal approach for cases in which the test conditions are not known and development data is not available for training a domain-specific calibration model.
Tracking the health of underwater species is critical to understanding the effects of climate change on marine ecosystems. Unfortunately, it's a time-consuming process -- biologists conduct studies with echosounders that use sonar to determine water and object depth, and they manually interpret the resulting 2D echograms. These interpretations are often prone to error and require pricey software like Echoview. Fortunately, a team of research scientists hailing from the University of Victoria in Canada are developing a machine learning method for detecting specific biological targets in acoustic survey data. In a preprint paper ("A Deep Learning based Framework for the Detection of Schools of Herring in Echograms"), they say that their approach -- which they tested on schools of herring -- might measurably improve the accuracy of environmental monitoring.
With the rise of artificial intelligence and voice recognition technology, there has been a plenty discussion about how industries, the labour force, and business models will change, but how will these technologies change the way consumers and brands interact? The explosion of smartphones and social media opened a new world of opportunities to communicate. Mobile messaging apps like Facebook Messenger, WhatsApp, and WeChat became the norm. Chatbots followed, which allowed brands to communicate on a one-to-one, personal level. Voice recognition erupted in the same way as social media and is completely changing the way we interact.
According to a new market report published by Credence Research Inc., "Global Voice Recognition Market (By Components (Hardware and Software), By Application (Artificial Intelligence and Non-Artificial Intelligence), By End-Use Vertical (Automobile, BFSI, Consumer, Government, Healthcare, Home, Security & Automation, and Others))- Growth, Share, Opportunities, Competitive Analysis, and Forecast 2018 – 2026", the worldwide market for voice recognition is anticipated to grow by 14.5 per cent CAGR during the 2018-2026 forecast period. Voice recognition schemes are safety solutions used to either grant or deny access to people by recognizing and matching their voice patterns. More and more biometric safety solutions have been deployed in the banking and finance sectors to improve safety and customer experience. Government agencies and businesses across the globe, on the other side, are adopting biometric techniques to thwart safety threats. Increased incidences of fraud in multiple industries and enhanced adoption of mobile banking, particularly among e-commerce distributors, are anticipated to drive the global voice recognition market.
McDonald's announced it will McBuy the Bay Area voice-recognition startup Apprente for an undisclosed amount. According to McDonald's, Apprente's "sound-to-meaning" technology handles "complex, multilingual, multi-accent and multi-item conversational ordering," and believes the technology will help streamline the drive-thru process -- even faster food, you say?? As the earth turns and the centuries change, so does the way people wish to order a Big Mac, and Micky D's has the cash to listen. Back in March, the company bought Dynamic Yield, which customizes drive-thru menus based on factors like weather, time of day, and customer order profiles. A month later, it invested in New Zealand app-designer Plexure, which will help connect customers to its new smart drive-thrus, among other things.