1,816 research outputs found
Attention-Based End-to-End Speech Recognition on Voice Search
Recently, there has been a growing interest in end-to-end speech recognition
that directly transcribes speech to text without any predefined alignments. In
this paper, we explore the use of attention-based encoder-decoder model for
Mandarin speech recognition on a voice search task. Previous attempts have
shown that applying attention-based encoder-decoder to Mandarin speech
recognition was quite difficult due to the logographic orthography of Mandarin,
the large vocabulary and the conditional dependency of the attention model. In
this paper, we use character embedding to deal with the large vocabulary.
Several tricks are used for effective model training, including L2
regularization, Gaussian weight noise and frame skipping. We compare two
attention mechanisms and use attention smoothing to cover long context in the
attention model. Taken together, these tricks allow us to finally achieve a
character error rate (CER) of 3.58% and a sentence error rate (SER) of 7.43% on
the MiTV voice search dataset. While together with a trigram language model,
CER and SER reach 2.81% and 5.77%, respectively
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
Improving the Performance of Online Neural Transducer Models
Having a sequence-to-sequence model which can operate in an online fashion is
important for streaming applications such as Voice Search. Neural transducer is
a streaming sequence-to-sequence model, but has shown a significant degradation
in performance compared to non-streaming models such as Listen, Attend and
Spell (LAS). In this paper, we present various improvements to NT.
Specifically, we look at increasing the window over which NT computes
attention, mainly by looking backwards in time so the model still remains
online. In addition, we explore initializing a NT model from a LAS-trained
model so that it is guided with a better alignment. Finally, we explore
including stronger language models such as using wordpiece models, and applying
an external LM during the beam search. On a Voice Search task, we find with
these improvements we can get NT to match the performance of LAS
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