The Academia Sinica Systems of Speech Recognition and Speaker Diarization for the CHiME-6 Challenge

Lee, Hung-Shin; Peng, Yuhuai; Huang, Pin-Tuan; Tseng, Ying-Chun; Wu, Chia-Hua; Tsao, Yu; Wang, Hsin‐Min

doi:10.21437/chime.2020-7

Cited by 2 publications

(1 citation statement)

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“…where α and β are weighting factors. In previous studies in [29,30,31], only the CE and MSE were jointly optimized in AM training. In our experiments, we set α to 5 as in most previous studies and heuristically set β to 0.2.…”

Section: Final Objective Functionmentioning

confidence: 99%

Speech-enhanced and Noise-aware Networks for Robust Speech Recognition

Lee¹,

Chen²,

Yu³

et al. 2022

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Compensation for channel mismatch and noise interference is essential for robust automatic speech recognition. Enhanced speech has been introduced into the multi-condition training of acoustic models to improve their generalization ability. In this paper, a noise-aware training framework based on two cascaded neural structures is proposed to jointly optimize speech enhancement and speech recognition. The feature enhancement module is composed of a multi-task autoencoder, where noisy speech is decomposed into clean speech and noise. By concatenating its enhanced, noise-aware, and noisy features for each frame, the acoustic-modeling module maps each featureaugmented frame into a triphone state by optimizing the latticefree maximum mutual information and cross entropy between the predicted and actual state sequences. On top of the factorized time delay neural network (TDNN-F) and its convolutional variant (CNN-TDNNF), both with SpecAug, the two proposed systems achieve word error rate (WER) of 3.90% and 3.55%, respectively, on the Aurora-4 task. Compared with the best existing systems that use bigram and trigram language models for decoding, the proposed CNN-TDNNF-based system achieves a relative WER reduction of 15.20% and 33.53%, respectively. In addition, the proposed CNN-TDNNF-based system also outperforms the baseline CNN-TDNNF system on the AMI task.

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Section: Final Objective Functionmentioning

confidence: 99%