2,278 research outputs found
Blind Normalization of Speech From Different Channels
We show how to construct a channel-independent representation of speech that
has propagated through a noisy reverberant channel. This is done by blindly
rescaling the cepstral time series by a non-linear function, with the form of
this scale function being determined by previously encountered cepstra from
that channel. The rescaled form of the time series is an invariant property of
it in the following sense: it is unaffected if the time series is transformed
by any time-independent invertible distortion. Because a linear channel with
stationary noise and impulse response transforms cepstra in this way, the new
technique can be used to remove the channel dependence of a cepstral time
series. In experiments, the method achieved greater channel-independence than
cepstral mean normalization, and it was comparable to the combination of
cepstral mean normalization and spectral subtraction, despite the fact that no
measurements of channel noise or reverberations were required (unlike spectral
subtraction).Comment: 25 pages, 7 figure
A minimax search algorithm for robust continuous speech recognition
In this paper, we propose a novel implementation of a minimax decision rule for continuous density hidden Markov-model-based robust speech recognition. By combining the idea of the minimax decision rule with a normal Viterbi search, we derive a recursive minimax search algorithm, where the minimax decision rule is repetitively applied to determine the partial paths during the search procedure. Because of the intrinsic nature of a recursive search, the proposed method can be easily extended to perform continuous speech recognition. Experimental results on Japanese isolated digits and TIDIGITS, where the mismatch between training and testing conditions is caused by additive white Gaussian noise, show the viability and efficiency of the proposed minimax search algorithm.published_or_final_versio
Bayesian distance metric learning and its application in automatic speaker recognition systems
This paper proposes state-of the-art Automatic Speaker Recognition System (ASR) based on Bayesian Distance Learning Metric as a feature extractor. In this modeling, I explored the constraints of the distance between modified and simplified i-vector pairs by the same speaker and different speakers. An approximation of the distance metric is used as a weighted covariance matrix from the higher eigenvectors of the covariance matrix, which is used to estimate the posterior distribution of the metric distance. Given a speaker tag, I select the data pair of the different speakers with the highest cosine score to form a set of speaker constraints. This collection captures the most discriminating variability between the speakers in the training data. This Bayesian distance learning approach achieves better performance than the most advanced methods. Furthermore, this method is insensitive to normalization compared to cosine scores. This method is very effective in the case of limited training data. The modified supervised i-vector based ASR system is evaluated on the NIST SRE 2008 database. The best performance of the combined cosine score EER 1.767% obtained using LDA200 + NCA200 + LDA200, and the best performance of Bayes_dml EER 1.775% obtained using LDA200 + NCA200 + LDA100. Bayesian_dml overcomes the combined norm of cosine scores and is the best result of the short2-short3 condition report for NIST SRE 2008 data
A Nonlinear Mixture Autoregressive Model For Speaker Verification
In this work, we apply a nonlinear mixture autoregressive (MixAR) model to supplant the Gaussian mixture model for speaker verification. MixAR is a statistical model that is a probabilistically weighted combination of components, each of which is an autoregressive filter in addition to a mean. The probabilistic mixing and the datadependent weights are responsible for the nonlinear nature of the model. Our experiments with synthetic as well as real speech data from standard speech corpora show that MixAR model outperforms GMM, especially under unseen noisy conditions. Moreover, MixAR did not require delta features and used 2.5x fewer parameters to achieve comparable or better performance as that of GMM using static as well as delta features. Also, MixAR suffered less from overitting issues than GMM when training data was sparse. However, MixAR performance deteriorated more quickly than that of GMM when evaluation data duration was reduced. This could pose limitations on the required minimum amount of evaluation data when using MixAR model for speaker verification
Modularity and Openness in Modeling Multi-Agent Systems
We revisit the formalism of modular interpreted systems (MIS) which
encourages modular and open modeling of synchronous multi-agent systems. The
original formulation of MIS did not live entirely up to its promise. In this
paper, we propose how to improve modularity and openness of MIS by changing the
structure of interference functions. These relatively small changes allow for
surprisingly high flexibility when modeling actual multi-agent systems. We
demonstrate this on two well-known examples, namely the trains, tunnel and
controller, and the dining cryptographers.
Perhaps more importantly, we propose how the notions of multi-agency and
openness, crucial for multi-agent systems, can be precisely defined based on
their MIS representations.Comment: In Proceedings GandALF 2013, arXiv:1307.416
On adaptive decision rules and decision parameter adaptation for automatic speech recognition
Recent advances in automatic speech recognition are accomplished by designing a plug-in maximum a posteriori decision rule such that the forms of the acoustic and language model distributions are specified and the parameters of the assumed distributions are estimated from a collection of speech and language training corpora. Maximum-likelihood point estimation is by far the most prevailing training method. However, due to the problems of unknown speech distributions, sparse training data, high spectral and temporal variabilities in speech, and possible mismatch between training and testing conditions, a dynamic training strategy is needed. To cope with the changing speakers and speaking conditions in real operational conditions for high-performance speech recognition, such paradigms incorporate a small amount of speaker and environment specific adaptation data into the training process. Bayesian adaptive learning is an optimal way to combine prior knowledge in an existing collection of general models with a new set of condition-specific adaptation data. In this paper, the mathematical framework for Bayesian adaptation of acoustic and language model parameters is first described. Maximum a posteriori point estimation is then developed for hidden Markov models and a number of useful parameters densities commonly used in automatic speech recognition and natural language processing.published_or_final_versio
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