The aim of this paper is to investigate the benefit of combining both language and acoustic modelling for speaker diarization. Although conventional systems only use acoustic features, in some scenarios linguistic data contain high discriminative speaker information, even more reliable than the acoustic ones. In this study we analyze how an appropriate fusion of both kind of features is able to obtain good results in these cases. The proposed system is based on an iterative algorithm where a LSTM network is used as a speaker classifier. The network is fed with character-level word embeddings and a GMM based acoustic score created with the output labels from previous iterations. The presented algorithm has been evaluated in a Call-Center database, which is composed of telephone interview audios. The combination of acoustic features and linguistic content shows a 84.29% improvement in terms of a word-level DER as compared to a HMM/VB baseline system. The results of this study confirms that linguistic content can be efficiently used for some speaker recognition tasks.

After years of research and development, Uniquify, a Silicon Valley neural network and AI edge computing company, is ready to unveil neural network 2.0 technology at the CES 2022 event. Currently, neural network technology is used in creating visual, audio, data, and natural language processing (NLP) models with the multiply-accumulate (MAC)-based operations. But with Uniquify's second-generation neural network 2.0 technology, neural networks shrink neurons by using proprietary AI processing elements (AIPEs) in place of MAC operations. AIPE technology shrinks the neurons in neural networks to enable the creation of the most advanced and complex AI visual, audio, and NLP models. In the past, MAC hardware was used to implement advanced but bulky neural network models, which severely hindered the possibilities of edge computing.

Uniquify, a Silicon Valley neural network technology and AI edge computing company, is announcing a proprietary neural network and AI modeling technology that introduces a new paradigm to transition consumer smart devices to consumer AI devices. The bottleneck to adopting advanced AI technology isn't the AI models or platforms but how to economically deploy these complex AI models for consumers at the edges. Uniquify's neural network 2.0 and AI modeling technology will enable many consumer products to become AI devices so that consumers can benefit from advanced AI models while protecting their privacy by running services at the edges. "We have seen many consumer devices like the phone, car, and TV go through a'smart' paradigm shift in the past few decades," says Josh Lee, CEO of Uniquify. "The world is ready for an'AI' paradigm shift to trigger replacement cycles in those consumer industries and more. I believe today's advanced AI models can be grafted into numerous consumer devices to provide richer experiences and enhanced capabilities for consumers. We believe we are ready to kickstart the'smart' to'AI' paradigm shift with our proprietary Neural Network 2.0 and AI modeling technology."

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