Causal speech enhancement using dynamical-weighted loss and attention encoder-decoder recurrent neural network.

ABSTRACT: Speech enhancement (SE) reduces background noise signals in target speech and is applied at the front end in various real-world applications, including robust ASRs and real-time processing in mobile phone communications. SE systems are commonly integrated into mobile phones to increase quality and intelligibility. As a result, a low-latency system is required to operate in real-world applications. On the other hand, these systems need efficient optimization. This research focuses on the single-microphone SE operating in real-time systems with better optimization. We propose a causal data-driven model that uses attention encoder-decoder long short-term memory (LSTM) to estimate the time-frequency mask from a noisy speech in order to make a clean speech for real-time applications that need l

SUBMITTER: Peracha FK 

PROVIDER: S-EPMC10174555 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature