6 research outputs found
Improving bottleneck features for Vietnamese large vocabulary continuous speech recognition system using deep neural networks
In this paper, the pre-training method based on denoising auto-encoder is investigated and proved to be good models for initializing bottleneck networks of Vietnamese speech recognition system that result in better recognition performance compared to base bottleneck features reported previously. The experiments are carried out on the dataset containing speeches on Voice of Vietnam channel (VOV). The results show that the DBNF extraction for Vietnamese recognition decreases relative word error rate by 14 % and 39 % compared to the base bottleneck features and MFCC baseline, respectively
Cross-Domain Adaptation of Spoken Language Identification for Related Languages: The Curious Case of Slavic Languages
State-of-the-art spoken language identification (LID) systems, which are
based on end-to-end deep neural networks, have shown remarkable success not
only in discriminating between distant languages but also between
closely-related languages or even different spoken varieties of the same
language. However, it is still unclear to what extent neural LID models
generalize to speech samples with different acoustic conditions due to domain
shift. In this paper, we present a set of experiments to investigate the impact
of domain mismatch on the performance of neural LID systems for a subset of six
Slavic languages across two domains (read speech and radio broadcast) and
examine two low-level signal descriptors (spectral and cepstral features) for
this task. Our experiments show that (1) out-of-domain speech samples severely
hinder the performance of neural LID models, and (2) while both spectral and
cepstral features show comparable performance within-domain, spectral features
show more robustness under domain mismatch. Moreover, we apply unsupervised
domain adaptation to minimize the discrepancy between the two domains in our
study. We achieve relative accuracy improvements that range from 9% to 77%
depending on the diversity of acoustic conditions in the source domain.Comment: To appear in INTERSPEECH 202