485 research outputs found
Multilingual Adaptation of RNN Based ASR Systems
In this work, we focus on multilingual systems based on recurrent neural
networks (RNNs), trained using the Connectionist Temporal Classification (CTC)
loss function. Using a multilingual set of acoustic units poses difficulties.
To address this issue, we proposed Language Feature Vectors (LFVs) to train
language adaptive multilingual systems. Language adaptation, in contrast to
speaker adaptation, needs to be applied not only on the feature level, but also
to deeper layers of the network. In this work, we therefore extended our
previous approach by introducing a novel technique which we call "modulation".
Based on this method, we modulated the hidden layers of RNNs using LFVs. We
evaluated this approach in both full and low resource conditions, as well as
for grapheme and phone based systems. Lower error rates throughout the
different conditions could be achieved by the use of the modulation.Comment: 5 pages, 1 figure, to appear in 2018 IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP 2018
Multilingual Speech Recognition With A Single End-To-End Model
Training a conventional automatic speech recognition (ASR) system to support
multiple languages is challenging because the sub-word unit, lexicon and word
inventories are typically language specific. In contrast, sequence-to-sequence
models are well suited for multilingual ASR because they encapsulate an
acoustic, pronunciation and language model jointly in a single network. In this
work we present a single sequence-to-sequence ASR model trained on 9 different
Indian languages, which have very little overlap in their scripts.
Specifically, we take a union of language-specific grapheme sets and train a
grapheme-based sequence-to-sequence model jointly on data from all languages.
We find that this model, which is not explicitly given any information about
language identity, improves recognition performance by 21% relative compared to
analogous sequence-to-sequence models trained on each language individually. By
modifying the model to accept a language identifier as an additional input
feature, we further improve performance by an additional 7% relative and
eliminate confusion between different languages.Comment: Accepted in ICASSP 201
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
Leveraging native language information for improved accented speech recognition
Recognition of accented speech is a long-standing challenge for automatic
speech recognition (ASR) systems, given the increasing worldwide population of
bi-lingual speakers with English as their second language. If we consider
foreign-accented speech as an interpolation of the native language (L1) and
English (L2), using a model that can simultaneously address both languages
would perform better at the acoustic level for accented speech. In this study,
we explore how an end-to-end recurrent neural network (RNN) trained system with
English and native languages (Spanish and Indian languages) could leverage data
of native languages to improve performance for accented English speech. To this
end, we examine pre-training with native languages, as well as multi-task
learning (MTL) in which the main task is trained with native English and the
secondary task is trained with Spanish or Indian Languages. We show that the
proposed MTL model performs better than the pre-training approach and
outperforms a baseline model trained simply with English data. We suggest a new
setting for MTL in which the secondary task is trained with both English and
the native language, using the same output set. This proposed scenario yields
better performance with +11.95% and +17.55% character error rate gains over
baseline for Hispanic and Indian accents, respectively.Comment: Accepted at Interspeech 201
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
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