188 research outputs found
Feature-space Speaker Adaptation for Probabilistic Linear Discriminant Analysis Acoustic Models
Probabilistic linear discriminant analysis (PLDA) acoustic models extend Gaussian mixture models by factorizing the acoustic variability using state-dependent and observation-dependent variables. This enables the use of higher dimensional acoustic features, and the capture of intra-frame feature corre-lations. In this paper, we investigate the estimation of speaker adaptive feature-space (constrained) maximum likelihood lin-ear regression transforms from PLDA-based acoustic models. This feature-space speaker transformation estimation approach is potentially very useful due to the ability of PLDA acoustic models to use different types of acoustic features, for example applying these transforms to deep neural network (DNN) acous-tic models for cross adaptation. We evaluated the approach on the Switchboard corpus, and observe significant word error re-duction by using both the mel-frequency cepstral coefficients and DNN bottleneck features. Index Terms: speech recognition, probabilistic linear discrim-inant analysis, speaker adaptation, fMLLR, PLD
Embedding-Based Speaker Adaptive Training of Deep Neural Networks
An embedding-based speaker adaptive training (SAT) approach is proposed and
investigated in this paper for deep neural network acoustic modeling. In this
approach, speaker embedding vectors, which are a constant given a particular
speaker, are mapped through a control network to layer-dependent element-wise
affine transformations to canonicalize the internal feature representations at
the output of hidden layers of a main network. The control network for
generating the speaker-dependent mappings is jointly estimated with the main
network for the overall speaker adaptive acoustic modeling. Experiments on
large vocabulary continuous speech recognition (LVCSR) tasks show that the
proposed SAT scheme can yield superior performance over the widely-used
speaker-aware training using i-vectors with speaker-adapted input features
DNN adaptation by automatic quality estimation of ASR hypotheses
In this paper we propose to exploit the automatic Quality Estimation (QE) of
ASR hypotheses to perform the unsupervised adaptation of a deep neural network
modeling acoustic probabilities. Our hypothesis is that significant
improvements can be achieved by: i)automatically transcribing the evaluation
data we are currently trying to recognise, and ii) selecting from it a subset
of "good quality" instances based on the word error rate (WER) scores predicted
by a QE component. To validate this hypothesis, we run several experiments on
the evaluation data sets released for the CHiME-3 challenge. First, we operate
in oracle conditions in which manual transcriptions of the evaluation data are
available, thus allowing us to compute the "true" sentence WER. In this
scenario, we perform the adaptation with variable amounts of data, which are
characterised by different levels of quality. Then, we move to realistic
conditions in which the manual transcriptions of the evaluation data are not
available. In this case, the adaptation is performed on data selected according
to the WER scores "predicted" by a QE component. Our results indicate that: i)
QE predictions allow us to closely approximate the adaptation results obtained
in oracle conditions, and ii) the overall ASR performance based on the proposed
QE-driven adaptation method is significantly better than the strong, most
recent, CHiME-3 baseline.Comment: Computer Speech & Language December 201
Automatic speech recognition with deep neural networks for impaired speech
The final publication is available at https://link.springer.com/chapter/10.1007%2F978-3-319-49169-1_10Automatic Speech Recognition has reached almost human performance in some controlled scenarios. However, recognition of impaired speech is a difficult task for two main reasons: data is (i) scarce and (ii) heterogeneous. In this work we train different architectures on a database of dysarthric speech. A comparison between architectures shows that, even with a small database, hybrid DNN-HMM models outperform classical GMM-HMM according to word error rate measures. A DNN is able to improve the recognition word error rate a 13% for subjects with dysarthria with respect to the best classical architecture. This improvement is higher than the one given by other deep neural networks such as CNNs, TDNNs and LSTMs. All the experiments have been done with the Kaldi toolkit for speech recognition for which we have adapted several recipes to deal with dysarthric speech and work on the TORGO database. These recipes are publicly available.Peer ReviewedPostprint (author's final draft
Improvements to deep convolutional neural networks for LVCSR
Deep Convolutional Neural Networks (CNNs) are more powerful than Deep Neural
Networks (DNN), as they are able to better reduce spectral variation in the
input signal. This has also been confirmed experimentally, with CNNs showing
improvements in word error rate (WER) between 4-12% relative compared to DNNs
across a variety of LVCSR tasks. In this paper, we describe different methods
to further improve CNN performance. First, we conduct a deep analysis comparing
limited weight sharing and full weight sharing with state-of-the-art features.
Second, we apply various pooling strategies that have shown improvements in
computer vision to an LVCSR speech task. Third, we introduce a method to
effectively incorporate speaker adaptation, namely fMLLR, into log-mel
features. Fourth, we introduce an effective strategy to use dropout during
Hessian-free sequence training. We find that with these improvements,
particularly with fMLLR and dropout, we are able to achieve an additional 2-3%
relative improvement in WER on a 50-hour Broadcast News task over our previous
best CNN baseline. On a larger 400-hour BN task, we find an additional 4-5%
relative improvement over our previous best CNN baseline.Comment: 6 pages, 1 figur
Porting concepts from DNNs back to GMMs
Deep neural networks (DNNs) have been shown to outperform Gaussian Mixture Models (GMM) on a variety of speech recognition benchmarks. In this paper we analyze the differences between the DNN and GMM modeling techniques and port the best ideas from the DNN-based modeling to a GMM-based system. By going both deep (multiple layers) and wide (multiple parallel sub-models) and by sharing model parameters, we are able to close the gap between the two modeling techniques on the TIMIT database. Since the 'deep' GMMs retain the maximum-likelihood trained Gaussians as first layer, advanced techniques such as speaker adaptation and model-based noise robustness can be readily incorporated. Regardless of their similarities, the DNNs and the deep GMMs still show a sufficient amount of complementarity to allow effective system combination
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