8 research outputs found
Streaming End-to-end Speech Recognition For Mobile Devices
End-to-end (E2E) models, which directly predict output character sequences
given input speech, are good candidates for on-device speech recognition. E2E
models, however, present numerous challenges: In order to be truly useful, such
models must decode speech utterances in a streaming fashion, in real time; they
must be robust to the long tail of use cases; they must be able to leverage
user-specific context (e.g., contact lists); and above all, they must be
extremely accurate. In this work, we describe our efforts at building an E2E
speech recognizer using a recurrent neural network transducer. In experimental
evaluations, we find that the proposed approach can outperform a conventional
CTC-based model in terms of both latency and accuracy in a number of evaluation
categories
Robust learning of acoustic representations from diverse speech data
Automatic speech recognition is increasingly applied to new domains. A key challenge is
to robustly learn, update and maintain representations to cope with transient acoustic
conditions. A typical example is broadcast media, for which speakers and environments
may change rapidly, and available supervision may be poor. The concern of this
thesis is to build and investigate methods for acoustic modelling that are robust to the
characteristics and transient conditions as embodied by such media.
The first contribution of the thesis is a technique to make use of inaccurate transcriptions as supervision for acoustic model training. There is an abundance of audio
with approximate labels, but training methods can be sensitive to label errors, and their
use is therefore not trivial. State-of-the-art semi-supervised training makes effective
use of a lattice of supervision, inherently encoding uncertainty in the labels to avoid
overfitting to poor supervision, but does not make use of the transcriptions. Existing
approaches that do aim to make use of the transcriptions typically employ an algorithm
to filter or combine the transcriptions with the recognition output from a seed model,
but the final result does not encode uncertainty. We propose a method to combine the
lattice output from a biased recognition pass with the transcripts, crucially preserving
uncertainty in the lattice where appropriate. This substantially reduces the word error
rate on a broadcast task.
The second contribution is a method to factorise representations for speakers and
environments so that they may be combined in novel combinations. In realistic scenarios,
the speaker or environment transform at test time might be unknown, or there may be
insufficient data to learn a joint transform. We show that in such cases, factorised, or
independent, representations are required to avoid deteriorating performance. Using
i-vectors, we factorise speaker or environment information using multi-condition training
with neural networks. Specifically, we extract bottleneck features from networks trained
to classify either speakers or environments. The resulting factorised representations
prove beneficial when one factor is missing at test time, or when all factors are seen,
but not in the desired combination.
The third contribution is an investigation of model adaptation in a longitudinal
setting. In this scenario, we repeatedly adapt a model to new data, with the constraint
that previous data becomes unavailable. We first demonstrate the effect of such a
constraint, and show that using a cyclical learning rate may help. We then observe
that these successive models lend themselves well to ensembling. Finally, we show
that the impact of this constraint in an active learning setting may be detrimental to
performance, and suggest to combine active learning with semi-supervised training to
avoid biasing the model.
The fourth contribution is a method to adapt low-level features in a parameter-efficient and interpretable manner. We propose to adapt the filters in a neural feature
extractor, known as SincNet. In contrast to traditional techniques that warp the
filterbank frequencies in standard feature extraction, adapting SincNet parameters is
more flexible and more readily optimised, whilst maintaining interpretability. On a task
adapting from adult to child speech, we show that this layer is well suited for adaptation
and is very effective with respect to the small number of adapted parameters