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Found 6 papers, 0 papers with code
SeMaScore : a new evaluation metric for automatic speech recognition tasks
In this study, we present SeMaScore, generated using a segment-wise mapping and scoring algorithm that serves as an evaluation metric for automatic speech recognition tasks.
Ed-Fed: A generic federated learning framework with resource-aware client selection for edge devices
Our evaluation has shown that the proposed approach significantly optimises waiting time in FL compared to conventional random client selection methods.
MobileASR: A resource-aware on-device learning framework for user voice personalization applications on mobile phones
We describe a comprehensive methodology for developing user-voice personalized automatic speech recognition (ASR) models by effectively training models on mobile phones, allowing user data and models to be stored and used locally.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
PreMa: Predictive Maintenance of Solenoid Valve in Real-Time at Embedded Edge-Level
In this work, we describe the construction of a smart and real-time edge-based electronic product called PreMa, which is basically a sensor for monitoring the health of a Solenoid Valve (SV).
A review of TinyML
In this current technological world, the application of machine learning is becoming ubiquitous.
H_eval: A new hybrid evaluation metric for automatic speech recognition tasks
We propose H_eval, a new hybrid evaluation metric for ASR systems that considers both semantic correctness and error rate and performs significantly well in scenarios where WER and SD perform poorly.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+8
