AuthorsSatyam Kumar, Sai Srujana Buddi, Utkarsh Oggy Sarawgi, Vineet Garg, Shivesh Ranjan, Ognjen (Oggi) Rudovic, Ahmed Hussen Abdelaziz, Saurabh Adya
Voice activity detection (VAD) is a critical component in various applications such as speech recognition, speaker identification, and hands-free communication systems. With the increasing demand for personalized and context-aware technologies, the need for effective personalized VAD systems has become paramount. In this paper, we present a comparative analysis of Personalized Voice Activity Detection (PVAD) systems to assess their real-world effectiveness. We introduce a comprehensive approach to assess PVAD systems, incorporating various performance metrics such as frame-level and utterance-level error Rates and Onset Detection latency, alongside user-level analysis. Through extensive experimentation and evaluation, we provide a thorough understanding of strengths and limitations of various PVAD variants. This paper advances the understanding of PVAD technology by offering insights into its efficacy and viability in practical applications using a comprehensive set of metrics.
A voice replicator is a powerful tool for people at risk of losing their ability to speak, including those with a recent diagnosis of amyotrophic lateral sclerosis (ALS) or other conditions that can progressively impact speaking ability. First introduced in May 2023 and made available on iOS 17 in September 2023, Personal Voice is a tool that creates a synthesized voice for such users to speak in FaceTime, phone calls, assistive communication apps, and in-person conversations.
A growing number of consumer devices, including smart speakers, headphones, and watches, use speech as the primary means of user input. As a result, voice trigger detection systems—a mechanism that uses voice recognition technology to control access to a particular device or feature—have become an important component of the user interaction pipeline as they signal the start of an interaction between the user and a device. Since these systems are deployed entirely on-device, several considerations inform their design, like privacy, latency, accuracy, and power consumption.
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