1,076 research outputs found
Multilingual Training and Cross-lingual Adaptation on CTC-based Acoustic Model
Multilingual models for Automatic Speech Recognition (ASR) are attractive as
they have been shown to benefit from more training data, and better lend
themselves to adaptation to under-resourced languages. However, initialisation
from monolingual context-dependent models leads to an explosion of
context-dependent states. Connectionist Temporal Classification (CTC) is a
potential solution to this as it performs well with monophone labels.
We investigate multilingual CTC in the context of adaptation and
regularisation techniques that have been shown to be beneficial in more
conventional contexts. The multilingual model is trained to model a universal
International Phonetic Alphabet (IPA)-based phone set using the CTC loss
function. Learning Hidden Unit Contribution (LHUC) is investigated to perform
language adaptive training. In addition, dropout during cross-lingual
adaptation is also studied and tested in order to mitigate the overfitting
problem.
Experiments show that the performance of the universal phoneme-based CTC
system can be improved by applying LHUC and it is extensible to new phonemes
during cross-lingual adaptation. Updating all the parameters shows consistent
improvement on limited data. Applying dropout during adaptation can further
improve the system and achieve competitive performance with Deep Neural Network
/ Hidden Markov Model (DNN/HMM) systems on limited data
Transfer learning of language-independent end-to-end ASR with language model fusion
This work explores better adaptation methods to low-resource languages using
an external language model (LM) under the framework of transfer learning. We
first build a language-independent ASR system in a unified sequence-to-sequence
(S2S) architecture with a shared vocabulary among all languages. During
adaptation, we perform LM fusion transfer, where an external LM is integrated
into the decoder network of the attention-based S2S model in the whole
adaptation stage, to effectively incorporate linguistic context of the target
language. We also investigate various seed models for transfer learning.
Experimental evaluations using the IARPA BABEL data set show that LM fusion
transfer improves performances on all target five languages compared with
simple transfer learning when the external text data is available. Our final
system drastically reduces the performance gap from the hybrid systems.Comment: Accepted at ICASSP201
Transfer Learning for Speech and Language Processing
Transfer learning is a vital technique that generalizes models trained for
one setting or task to other settings or tasks. For example in speech
recognition, an acoustic model trained for one language can be used to
recognize speech in another language, with little or no re-training data.
Transfer learning is closely related to multi-task learning (cross-lingual vs.
multilingual), and is traditionally studied in the name of `model adaptation'.
Recent advance in deep learning shows that transfer learning becomes much
easier and more effective with high-level abstract features learned by deep
models, and the `transfer' can be conducted not only between data distributions
and data types, but also between model structures (e.g., shallow nets and deep
nets) or even model types (e.g., Bayesian models and neural models). This
review paper summarizes some recent prominent research towards this direction,
particularly for speech and language processing. We also report some results
from our group and highlight the potential of this very interesting research
field.Comment: 13 pages, APSIPA 201
Acoustic Word Embeddings for Zero-Resource Languages Using Self-Supervised Contrastive Learning and Multilingual Adaptation
Acoustic word embeddings (AWEs) are fixed-dimensional representations of
variable-length speech segments. For zero-resource languages where labelled
data is not available, one AWE approach is to use unsupervised
autoencoder-based recurrent models. Another recent approach is to use
multilingual transfer: a supervised AWE model is trained on several
well-resourced languages and then applied to an unseen zero-resource language.
We consider how a recent contrastive learning loss can be used in both the
purely unsupervised and multilingual transfer settings. Firstly, we show that
terms from an unsupervised term discovery system can be used for contrastive
self-supervision, resulting in improvements over previous unsupervised
monolingual AWE models. Secondly, we consider how multilingual AWE models can
be adapted to a specific zero-resource language using discovered terms. We find
that self-supervised contrastive adaptation outperforms adapted multilingual
correspondence autoencoder and Siamese AWE models, giving the best overall
results in a word discrimination task on six zero-resource languages.Comment: Accepted to SLT 202
Neural approaches to spoken content embedding
Comparing spoken segments is a central operation to speech processing.
Traditional approaches in this area have favored frame-level dynamic
programming algorithms, such as dynamic time warping, because they require no
supervision, but they are limited in performance and efficiency. As an
alternative, acoustic word embeddings -- fixed-dimensional vector
representations of variable-length spoken word segments -- have begun to be
considered for such tasks as well. However, the current space of such
discriminative embedding models, training approaches, and their application to
real-world downstream tasks is limited. We start by considering ``single-view"
training losses where the goal is to learn an acoustic word embedding model
that separates same-word and different-word spoken segment pairs. Then, we
consider ``multi-view" contrastive losses. In this setting, acoustic word
embeddings are learned jointly with embeddings of character sequences to
generate acoustically grounded embeddings of written words, or acoustically
grounded word embeddings.
In this thesis, we contribute new discriminative acoustic word embedding
(AWE) and acoustically grounded word embedding (AGWE) approaches based on
recurrent neural networks (RNNs). We improve model training in terms of both
efficiency and performance. We take these developments beyond English to
several low-resource languages and show that multilingual training improves
performance when labeled data is limited. We apply our embedding models, both
monolingual and multilingual, to the downstream tasks of query-by-example
speech search and automatic speech recognition. Finally, we show how our
embedding approaches compare with and complement more recent self-supervised
speech models.Comment: PhD thesi
Transfer Learning for Speech Recognition on a Budget
End-to-end training of automated speech recognition (ASR) systems requires
massive data and compute resources. We explore transfer learning based on model
adaptation as an approach for training ASR models under constrained GPU memory,
throughput and training data. We conduct several systematic experiments
adapting a Wav2Letter convolutional neural network originally trained for
English ASR to the German language. We show that this technique allows faster
training on consumer-grade resources while requiring less training data in
order to achieve the same accuracy, thereby lowering the cost of training ASR
models in other languages. Model introspection revealed that small adaptations
to the network's weights were sufficient for good performance, especially for
inner layers.Comment: Accepted for 2nd ACL Workshop on Representation Learning for NL
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