8,885 research outputs found
Context-Dependent Acoustic Modeling without Explicit Phone Clustering
Phoneme-based acoustic modeling of large vocabulary automatic speech
recognition takes advantage of phoneme context. The large number of
context-dependent (CD) phonemes and their highly varying statistics require
tying or smoothing to enable robust training. Usually, Classification and
Regression Trees are used for phonetic clustering, which is standard in Hidden
Markov Model (HMM)-based systems. However, this solution introduces a secondary
training objective and does not allow for end-to-end training. In this work, we
address a direct phonetic context modeling for the hybrid Deep Neural Network
(DNN)/HMM, that does not build on any phone clustering algorithm for the
determination of the HMM state inventory. By performing different
decompositions of the joint probability of the center phoneme state and its
left and right contexts, we obtain a factorized network consisting of different
components, trained jointly. Moreover, the representation of the phonetic
context for the network relies on phoneme embeddings. The recognition accuracy
of our proposed models on the Switchboard task is comparable and outperforms
slightly the hybrid model using the standard state-tying decision trees.Comment: Submitted to Interspeech 202
A Subband-Based SVM Front-End for Robust ASR
This work proposes a novel support vector machine (SVM) based robust
automatic speech recognition (ASR) front-end that operates on an ensemble of
the subband components of high-dimensional acoustic waveforms. The key issues
of selecting the appropriate SVM kernels for classification in frequency
subbands and the combination of individual subband classifiers using ensemble
methods are addressed. The proposed front-end is compared with state-of-the-art
ASR front-ends in terms of robustness to additive noise and linear filtering.
Experiments performed on the TIMIT phoneme classification task demonstrate the
benefits of the proposed subband based SVM front-end: it outperforms the
standard cepstral front-end in the presence of noise and linear filtering for
signal-to-noise ratio (SNR) below 12-dB. A combination of the proposed
front-end with a conventional front-end such as MFCC yields further
improvements over the individual front ends across the full range of noise
levels
Exploiting Low-dimensional Structures to Enhance DNN Based Acoustic Modeling in Speech Recognition
We propose to model the acoustic space of deep neural network (DNN)
class-conditional posterior probabilities as a union of low-dimensional
subspaces. To that end, the training posteriors are used for dictionary
learning and sparse coding. Sparse representation of the test posteriors using
this dictionary enables projection to the space of training data. Relying on
the fact that the intrinsic dimensions of the posterior subspaces are indeed
very small and the matrix of all posteriors belonging to a class has a very low
rank, we demonstrate how low-dimensional structures enable further enhancement
of the posteriors and rectify the spurious errors due to mismatch conditions.
The enhanced acoustic modeling method leads to improvements in continuous
speech recognition task using hybrid DNN-HMM (hidden Markov model) framework in
both clean and noisy conditions, where upto 15.4% relative reduction in word
error rate (WER) is achieved
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
Smoothed Analysis in Unsupervised Learning via Decoupling
Smoothed analysis is a powerful paradigm in overcoming worst-case
intractability in unsupervised learning and high-dimensional data analysis.
While polynomial time smoothed analysis guarantees have been obtained for
worst-case intractable problems like tensor decompositions and learning
mixtures of Gaussians, such guarantees have been hard to obtain for several
other important problems in unsupervised learning. A core technical challenge
in analyzing algorithms is obtaining lower bounds on the least singular value
for random matrix ensembles with dependent entries, that are given by
low-degree polynomials of a few base underlying random variables.
In this work, we address this challenge by obtaining high-confidence lower
bounds on the least singular value of new classes of structured random matrix
ensembles of the above kind. We then use these bounds to design algorithms with
polynomial time smoothed analysis guarantees for the following three important
problems in unsupervised learning:
1. Robust subspace recovery, when the fraction of inliers in the
d-dimensional subspace is at least for any constant integer . This contrasts with the known
worst-case intractability when , and the previous smoothed
analysis result which needed (Hardt and Moitra, 2013).
2. Learning overcomplete hidden markov models, where the size of the state
space is any polynomial in the dimension of the observations. This gives the
first polynomial time guarantees for learning overcomplete HMMs in a smoothed
analysis model.
3. Higher order tensor decompositions, where we generalize the so-called
FOOBI algorithm of Cardoso to find order- rank-one tensors in a subspace.
This allows us to obtain polynomially robust decomposition algorithms for
'th order tensors with rank .Comment: 44 page
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