354 research outputs found
No Need for a Lexicon? Evaluating the Value of the Pronunciation Lexica in End-to-End Models
For decades, context-dependent phonemes have been the dominant sub-word unit
for conventional acoustic modeling systems. This status quo has begun to be
challenged recently by end-to-end models which seek to combine acoustic,
pronunciation, and language model components into a single neural network. Such
systems, which typically predict graphemes or words, simplify the recognition
process since they remove the need for a separate expert-curated pronunciation
lexicon to map from phoneme-based units to words. However, there has been
little previous work comparing phoneme-based versus grapheme-based sub-word
units in the end-to-end modeling framework, to determine whether the gains from
such approaches are primarily due to the new probabilistic model, or from the
joint learning of the various components with grapheme-based units.
In this work, we conduct detailed experiments which are aimed at quantifying
the value of phoneme-based pronunciation lexica in the context of end-to-end
models. We examine phoneme-based end-to-end models, which are contrasted
against grapheme-based ones on a large vocabulary English Voice-search task,
where we find that graphemes do indeed outperform phonemes. We also compare
grapheme and phoneme-based approaches on a multi-dialect English task, which
once again confirm the superiority of graphemes, greatly simplifying the system
for recognizing multiple dialects
Multi-Dialect Speech Recognition With A Single Sequence-To-Sequence Model
Sequence-to-sequence models provide a simple and elegant solution for
building speech recognition systems by folding separate components of a typical
system, namely acoustic (AM), pronunciation (PM) and language (LM) models into
a single neural network. In this work, we look at one such sequence-to-sequence
model, namely listen, attend and spell (LAS), and explore the possibility of
training a single model to serve different English dialects, which simplifies
the process of training multi-dialect systems without the need for separate AM,
PM and LMs for each dialect. We show that simply pooling the data from all
dialects into one LAS model falls behind the performance of a model fine-tuned
on each dialect. We then look at incorporating dialect-specific information
into the model, both by modifying the training targets by inserting the dialect
symbol at the end of the original grapheme sequence and also feeding a 1-hot
representation of the dialect information into all layers of the model.
Experimental results on seven English dialects show that our proposed system is
effective in modeling dialect variations within a single LAS model,
outperforming a LAS model trained individually on each of the seven dialects by
3.1 ~ 16.5% relative.Comment: submitted to ICASSP 201
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
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