Comparative Analysis of Personalized Voice Activity Detection Systems: Assessing Real-World Effectiveness
Voice activity detection (VAD) is a critical component in various applications such as speech recognition, speech enhancement, and hands-free communication systems. With the increasing demand for personalized and context-aware technologies, the need for effective personalized VAD systems has become...
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| creator | Kumar, Satyam Buddi, Sai Srujana Sarawgi, Utkarsh Oggy Garg, Vineet Ranjan, Shivesh Ognjen Rudovic Abdelaziz, Ahmed Hussen Adya, Saurabh |
| description | Voice activity detection (VAD) is a critical component in various
applications such as speech recognition, speech enhancement, 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, detection latency and accuracy, alongside
user-level analysis. Through extensive experimentation and evaluation, we
provide a thorough understanding of the 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. |
| doi_str_mv | 10.48550/arxiv.2406.09443 |
| format | Article |
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applications such as speech recognition, speech enhancement, 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, detection latency and accuracy, alongside
user-level analysis. Through extensive experimentation and evaluation, we
provide a thorough understanding of the 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
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applications such as speech recognition, speech enhancement, 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, detection latency and accuracy, alongside
user-level analysis. Through extensive experimentation and evaluation, we
provide a thorough understanding of the 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
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applications such as speech recognition, speech enhancement, 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, detection latency and accuracy, alongside
user-level analysis. Through extensive experimentation and evaluation, we
provide a thorough understanding of the 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.</abstract><doi>10.48550/arxiv.2406.09443</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Human-Computer Interaction Computer Science - Learning |
| title | Comparative Analysis of Personalized Voice Activity Detection Systems: Assessing Real-World Effectiveness |
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