5,805 research outputs found
Leveraging Speaker Embeddings with Adversarial Multi-task Learning for Age Group Classification
Recently, researchers have utilized neural network-based speaker embedding
techniques in speaker-recognition tasks to identify speakers accurately.
However, speaker-discriminative embeddings do not always represent speech
features such as age group well. In an embedding model that has been highly
trained to capture speaker traits, the task of age group classification is
closer to speech information leakage. Hence, to improve age group
classification performance, we consider the use of speaker-discriminative
embeddings derived from adversarial multi-task learning to align features and
reduce the domain discrepancy in age subgroups. In addition, we investigated
different types of speaker embeddings to learn and generalize the
domain-invariant representations for age groups. Experimental results on the
VoxCeleb Enrichment dataset verify the effectiveness of our proposed adaptive
adversarial network in multi-objective scenarios and leveraging speaker
embeddings for the domain adaptation task
Speaker- and Age-Invariant Training for Child Acoustic Modeling Using Adversarial Multi-Task Learning
One of the major challenges in acoustic modelling of child speech is the
rapid changes that occur in the children's articulators as they grow up, their
differing growth rates and the subsequent high variability in the same age
group. These high acoustic variations along with the scarcity of child speech
corpora have impeded the development of a reliable speech recognition system
for children. In this paper, a speaker- and age-invariant training approach
based on adversarial multi-task learning is proposed. The system consists of
one generator shared network that learns to generate speaker- and age-invariant
features connected to three discrimination networks, for phoneme, age, and
speaker. The generator network is trained to minimize the
phoneme-discrimination loss and maximize the speaker- and age-discrimination
losses in an adversarial multi-task learning fashion. The generator network is
a Time Delay Neural Network (TDNN) architecture while the three discriminators
are feed-forward networks. The system was applied to the OGI speech corpora and
achieved a 13% reduction in the WER of the ASR.Comment: Submitted to ICASSP202
Adversarial Speaker Adaptation
We propose a novel adversarial speaker adaptation (ASA) scheme, in which
adversarial learning is applied to regularize the distribution of deep hidden
features in a speaker-dependent (SD) deep neural network (DNN) acoustic model
to be close to that of a fixed speaker-independent (SI) DNN acoustic model
during adaptation. An additional discriminator network is introduced to
distinguish the deep features generated by the SD model from those produced by
the SI model. In ASA, with a fixed SI model as the reference, an SD model is
jointly optimized with the discriminator network to minimize the senone
classification loss, and simultaneously to mini-maximize the SI/SD
discrimination loss on the adaptation data. With ASA, a senone-discriminative
deep feature is learned in the SD model with a similar distribution to that of
the SI model. With such a regularized and adapted deep feature, the SD model
can perform improved automatic speech recognition on the target speaker's
speech. Evaluated on the Microsoft short message dictation dataset, ASA
achieves 14.4% and 7.9% relative word error rate improvements for supervised
and unsupervised adaptation, respectively, over an SI model trained from 2600
hours data, with 200 adaptation utterances per speaker.Comment: 5 pages, 2 figures, ICASSP 201
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