2,154 research outputs found
Discriminative Segmental Cascades for Feature-Rich Phone Recognition
Discriminative segmental models, such as segmental conditional random fields
(SCRFs) and segmental structured support vector machines (SSVMs), have had
success in speech recognition via both lattice rescoring and first-pass
decoding. However, such models suffer from slow decoding, hampering the use of
computationally expensive features, such as segment neural networks or other
high-order features. A typical solution is to use approximate decoding, either
by beam pruning in a single pass or by beam pruning to generate a lattice
followed by a second pass. In this work, we study discriminative segmental
models trained with a hinge loss (i.e., segmental structured SVMs). We show
that beam search is not suitable for learning rescoring models in this
approach, though it gives good approximate decoding performance when the model
is already well-trained. Instead, we consider an approach inspired by
structured prediction cascades, which use max-marginal pruning to generate
lattices. We obtain a high-accuracy phonetic recognition system with several
expensive feature types: a segment neural network, a second-order language
model, and second-order phone boundary features
Duration modeling with expanded HMM applied to speech recognition
The occupancy of the HMM states is modeled by means of a Markov chain. A linear estimator is introduced to compute the probabilities of the Markov chain. The distribution function (DF) represents accurately the observed data. Representing the DF as a Markov chain allows the use of standard HMM recognizers. The increase of complexity is negligible in training and strongly limited during recognition. Experiments performed on acoustic-phonetic decoding shows how the phone recognition rate increases from 60.6 to 61.1. Furthermore, on a task of database inquires, where phones are used as subword units, the correct word rate increases from 88.2 to 88.4.Peer ReviewedPostprint (published version
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
Bayesian adaptive learning of the parameters of hidden Markov model for speech recognition
A theoretical framework for Bayesian adaptive training of the parameters of a discrete hidden Markov model (DHMM) and of a semi-continuous HMM (SCHMM) with Gaussian mixture state observation densities is presented. In addition to formulating the forward-backward MAP (maximum a posteriori) and the segmental MAP algorithms for estimating the above HMM parameters, a computationally efficient segmental quasi-Bayes algorithm for estimating the state-specific mixture coefficients in SCHMM is developed. For estimating the parameters of the prior densities, a new empirical Bayes method based on the moment estimates is also proposed. The MAP algorithms and the prior parameter specification are directly applicable to training speaker adaptive HMMs. Practical issues related to the use of the proposed techniques for HMM-based speaker adaptation are studied. The proposed MAP algorithms are shown to be effective especially in the cases in which the training or adaptation data are limited.published_or_final_versio
Phonetic and prosodic analysis of speech
In order to cope with the problems of spontaneous speech (including, for example, hesitations and non-words) it is necessary to extract from the speech signal all information it contains. Modeling of words by segmental units should be supported by suprasegmental units since valuable information is represented in the prosody of an utterance. We present an approach to flexible and efficient modeling of speech by segmental units and describe extraction and use of suprasegmental information
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