47 research outputs found
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
Multitask Learning with CTC and Segmental CRF for Speech Recognition
Segmental conditional random fields (SCRFs) and connectionist temporal
classification (CTC) are two sequence labeling methods used for end-to-end
training of speech recognition models. Both models define a transcription
probability by marginalizing decisions about latent segmentation alternatives
to derive a sequence probability: the former uses a globally normalized joint
model of segment labels and durations, and the latter classifies each frame as
either an output symbol or a "continuation" of the previous label. In this
paper, we train a recognition model by optimizing an interpolation between the
SCRF and CTC losses, where the same recurrent neural network (RNN) encoder is
used for feature extraction for both outputs. We find that this multitask
objective improves recognition accuracy when decoding with either the SCRF or
CTC models. Additionally, we show that CTC can also be used to pretrain the RNN
encoder, which improves the convergence rate when learning the joint model.Comment: 5 pages, 2 figures, camera ready version at Interspeech 201
Efficient Implementation of the Room Simulator for Training Deep Neural Network Acoustic Models
In this paper, we describe how to efficiently implement an acoustic room
simulator to generate large-scale simulated data for training deep neural
networks. Even though Google Room Simulator in [1] was shown to be quite
effective in reducing the Word Error Rates (WERs) for far-field applications by
generating simulated far-field training sets, it requires a very large number
of Fast Fourier Transforms (FFTs) of large size. Room Simulator in [1] used
approximately 80 percent of Central Processing Unit (CPU) usage in our CPU +
Graphics Processing Unit (GPU) training architecture [2]. In this work, we
implement an efficient OverLap Addition (OLA) based filtering using the
open-source FFTW3 library. Further, we investigate the effects of the Room
Impulse Response (RIR) lengths. Experimentally, we conclude that we can cut the
tail portions of RIRs whose power is less than 20 dB below the maximum power
without sacrificing the speech recognition accuracy. However, we observe that
cutting RIR tail more than this threshold harms the speech recognition accuracy
for rerecorded test sets. Using these approaches, we were able to reduce CPU
usage for the room simulator portion down to 9.69 percent in CPU/GPU training
architecture. Profiling result shows that we obtain 22.4 times speed-up on a
single machine and 37.3 times speed up on Google's distributed training
infrastructure.Comment: Published at INTERSPEECH 2018.
(https://www.isca-speech.org/archive/Interspeech_2018/abstracts/2566.html
Sampling from Stochastic Finite Automata with Applications to CTC Decoding
Stochastic finite automata arise naturally in many language and speech
processing tasks. They include stochastic acceptors, which represent certain
probability distributions over random strings. We consider the problem of
efficient sampling: drawing random string variates from the probability
distribution represented by stochastic automata and transformations of those.
We show that path-sampling is effective and can be efficient if the
epsilon-graph of a finite automaton is acyclic. We provide an algorithm that
ensures this by conflating epsilon-cycles within strongly connected components.
Sampling is also effective in the presence of non-injective transformations of
strings. We illustrate this in the context of decoding for Connectionist
Temporal Classification (CTC), where the predictive probabilities yield
auxiliary sequences which are transformed into shorter labeling strings. We can
sample efficiently from the transformed labeling distribution and use this in
two different strategies for finding the most probable CTC labeling
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