37,081 research outputs found
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
Towards Language-Universal End-to-End Speech Recognition
Building speech recognizers in multiple languages typically involves
replicating a monolingual training recipe for each language, or utilizing a
multi-task learning approach where models for different languages have separate
output labels but share some internal parameters. In this work, we exploit
recent progress in end-to-end speech recognition to create a single
multilingual speech recognition system capable of recognizing any of the
languages seen in training. To do so, we propose the use of a universal
character set that is shared among all languages. We also create a
language-specific gating mechanism within the network that can modulate the
network's internal representations in a language-specific way. We evaluate our
proposed approach on the Microsoft Cortana task across three languages and show
that our system outperforms both the individual monolingual systems and systems
built with a multi-task learning approach. We also show that this model can be
used to initialize a monolingual speech recognizer, and can be used to create a
bilingual model for use in code-switching scenarios.Comment: submitted to ICASSP 201
One-To-Many Multilingual End-to-end Speech Translation
Nowadays, training end-to-end neural models for spoken language translation
(SLT) still has to confront with extreme data scarcity conditions. The existing
SLT parallel corpora are indeed orders of magnitude smaller than those
available for the closely related tasks of automatic speech recognition (ASR)
and machine translation (MT), which usually comprise tens of millions of
instances. To cope with data paucity, in this paper we explore the
effectiveness of transfer learning in end-to-end SLT by presenting a
multilingual approach to the task. Multilingual solutions are widely studied in
MT and usually rely on ``\textit{target forcing}'', in which multilingual
parallel data are combined to train a single model by prepending to the input
sequences a language token that specifies the target language. However, when
tested in speech translation, our experiments show that MT-like \textit{target
forcing}, used as is, is not effective in discriminating among the target
languages. Thus, we propose a variant that uses target-language embeddings to
shift the input representations in different portions of the space according to
the language, so to better support the production of output in the desired
target language. Our experiments on end-to-end SLT from English into six
languages show important improvements when translating into similar languages,
especially when these are supported by scarce data. Further improvements are
obtained when using English ASR data as an additional language (up to
BLEU points).Comment: 8 pages, one figure, version accepted at ASRU 201
A Unified Multilingual Handwriting Recognition System using multigrams sub-lexical units
We address the design of a unified multilingual system for handwriting
recognition. Most of multi- lingual systems rests on specialized models that
are trained on a single language and one of them is selected at test time.
While some recognition systems are based on a unified optical model, dealing
with a unified language model remains a major issue, as traditional language
models are generally trained on corpora composed of large word lexicons per
language. Here, we bring a solution by con- sidering language models based on
sub-lexical units, called multigrams. Dealing with multigrams strongly reduces
the lexicon size and thus decreases the language model complexity. This makes
pos- sible the design of an end-to-end unified multilingual recognition system
where both a single optical model and a single language model are trained on
all the languages. We discuss the impact of the language unification on each
model and show that our system reaches state-of-the-art methods perfor- mance
with a strong reduction of the complexity.Comment: preprin
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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