Lijuan Shi
ABSTRACT
The recognition of mixed-bandwidth audio presents a challenge for both academic and industrial fields, with potentially greater implications for the latter. In this paper, we present a unified ASR architecture for mixed-bandwidth audio's recognition, here we innovatively propose to use the generative adversarial network and two discriminators to help achieving the ability of recognizing mixed sampling audio and guaranteeing the performance of ASR system. Through the adaptive training process of trained generator and ASR system, the performance can be further improved. We conduct experiments on the libri-speech dataset and demonstrate that our method can successfully recognize mixed-bandwidth audio and improve the accuracy of the ASR system by 3.65% in the narrowband data. Overall, the proposed unified ASR architecture provides a promising solution for the recognition of mixed-bandwidth audio in various settings.
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Index Terms
- A Unified Mixed-Bandwidth ASR Framework with Generative Adversarial Network
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