9,088 research outputs found
Genetic algorithms in speech recognition systems
Abstract In speech recognition, the training process plays an important role. When a good training model for a speech pattern is obtained, this not only enhances the speed of recognition tremendously .but also improves the quality of the overall performance in recognizing the speech utterance. In general, there are two classic approaches for this development, namely Dynamic Time Warping (DTW) and Hidden Markov Model (HMM). In this article, Genetic Algorithm (GA) is applied to solve involved nonlinear, discrete and constrained problems for DTW .Because of the intrinsic properties of GA, the associated non trival K-best paths of DTW can be identified without extra computational cost. The obtained results show the important contribution of the genetic algorithms in temporal alignment through the increasingly small factor of distortion
Why has (reasonably accurate) Automatic Speech Recognition been so hard to achieve?
Hidden Markov models (HMMs) have been successfully applied to automatic
speech recognition for more than 35 years in spite of the fact that a key HMM
assumption -- the statistical independence of frames -- is obviously violated
by speech data. In fact, this data/model mismatch has inspired many attempts to
modify or replace HMMs with alternative models that are better able to take
into account the statistical dependence of frames. However it is fair to say
that in 2010 the HMM is the consensus model of choice for speech recognition
and that HMMs are at the heart of both commercially available products and
contemporary research systems. In this paper we present a preliminary
exploration aimed at understanding how speech data depart from HMMs and what
effect this departure has on the accuracy of HMM-based speech recognition. Our
analysis uses standard diagnostic tools from the field of statistics --
hypothesis testing, simulation and resampling -- which are rarely used in the
field of speech recognition. Our main result, obtained by novel manipulations
of real and resampled data, demonstrates that real data have statistical
dependency and that this dependency is responsible for significant numbers of
recognition errors. We also demonstrate, using simulation and resampling, that
if we `remove' the statistical dependency from data, then the resulting
recognition error rates become negligible. Taken together, these results
suggest that a better understanding of the structure of the statistical
dependency in speech data is a crucial first step towards improving HMM-based
speech recognition
Multi-biometric templates using fingerprint and voice
As biometrics gains popularity, there is an increasing concern about privacy and misuse of biometric data held in central repositories. Furthermore, biometric verification systems face challenges arising from noise and intra-class variations. To tackle both problems, a multimodal biometric verification system combining fingerprint and voice modalities is proposed. The system combines the two modalities at the template level, using multibiometric templates. The fusion of fingerprint and voice data successfully diminishes privacy concerns by hiding the minutiae points from the fingerprint, among the artificial points generated by the features obtained from the spoken utterance of the speaker. Equal error rates are observed to be under 2% for the system where 600 utterances from 30 people have been processed and fused with a database of 400 fingerprints from 200 individuals. Accuracy is increased compared to the previous results for voice verification over the same speaker database
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