Speaker recognition is a well known and studied task in the speech processing domain. It has many applications, either for security or speaker adaptation of personal devices. In this paper, we present a new paradigm for automatic speaker recognition that we call Interactive Speaker Recognition (ISR). In this paradigm, the recognition system aims to incrementally build a representation of the speakers by requesting personalized utterances to be spoken in contrast to the standard text-dependent or text-independent schemes. To do so, we cast the speaker recognition task into a sequential decision-making problem that we solve with Reinforcement Learning. Using a standard dataset, we show that our method achieves excellent performance while using little speech signal amounts. This method could also be applied as an utterance selection mechanism for building speech synthesis systems.
Artificial intelligence has made major strides in the past few years, but those rapid advances are now raising some big ethical conundrums. Chief among them is the way machine learning can identify people's faces in photos and video footage with great accuracy. This might let you unlock your phone with a smile, but it also means that governments and big corporations have been given a powerful new surveillance tool. A new report from the AI Now Institute (large PDF), an influential research institute based in New York, has just identified facial recognition as a key challenge for society and policymakers. The speed at which facial recognition has grown comes down to the rapid development of a type of machine learning known as deep learning.
Image recognition is the process of identifying and detecting an object or a feature in a digital image or video. This concept is used in many applications like systems for factory automation, toll booth monitoring, and security surveillance. Specific image recognition applications include classifying digits using HOG features and an SVM classifier (Figure 1). Cross correlation can be used for pattern matching and target tracking as shown in Figure 2. An effective approach for image recognition includes using a technical computing environment for data analysis, visualization, and algorithm development.