Multi Filter Bank Approach for Speaker Verification Based on Genetic Algorithm

Charbuillet, Christophe; Gas, Bruno; Chétouani, Mohamed; Zarader, Jean Luc

doi:10.1007/978-3-540-77347-4_7

Cited by 5 publications

(6 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the resolution that these filterbanks provide below 2 kHz is higher, probably because this is the place for the two first formant frequencies. In contrast, when polynomial functions were used to encode the parameters [13], the obtained filterbanks were not regular and did not always cover most of the frequency band of interest. This may be attributed to the complex relation between filterbank parameters and the optimized polynomials.…”

Section: Optimization Of Central Frequenciesmentioning

confidence: 92%

“…An evolution strategy was also proposed in [12], but in this case for the optimization of a wavelet packet-based representation. In another evolutionary approach, for the task of speaker verification, polynomial functions were used to encode the parameters of the filterbanks, reducing the number of optimization parameters [13]. However, a complex relation between the polynomial coefficients and the filterbank parameters was proposed, and the combination of multiple optimized filterbanks and classifiers requires important changes in a standard ASR system.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evolutionary Splines for Cepstral Filterbank Optimization in Phoneme Classification

Vignolo

Rufiner

Milone

et al. 2011

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

Mel-frequency cepstral coefficients have long been the most widely used type of speech representation. They were introduced to incorporate biologically inspired characteristics into artificial speech recognizers. Recently, the introduction of new alternatives to the classic mel-scaled filterbank has led to improvements in the performance of phoneme recognition in adverse conditions. In this work we propose a new bioinspired approach for the optimization of the filterbanks, in order to find a robust speech representation. Our approach-which relies on evolutionary algorithms-reduces the number of parameters to optimize by using spline functions to shape the filterbanks. The success rates of a phoneme classifier based on hidden Markov models are used as the fitness measure, evaluated over the well-known TIMIT database. The results show that the proposed method is able to find optimized filterbanks for phoneme recognition, which significantly increases the robustness in adverse conditions.

show abstract

Section: Optimization Of Central Frequenciesmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Evolutionary Splines for Cepstral Filterbank Optimization in Phoneme Classification

Vignolo

Rufiner

Milone

et al. 2011

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

show abstract

“…This last element in the chromosome indicates the number of active filters. In other approaches (Charbuillet et al, 2007b), polynomial functions were used to encode the parameters which were optimized. Here, in contrast, all the parameters are directly coded in the chromosome.…”

Section: Genetically Optimized Cepstral Coefficientsmentioning

confidence: 99%

“…A filterbank is optimal if it results in a better speech signal parameterization, improving phoneme classification results. Similar approaches have been applied for other tasks such as speaker verification (Charbuillet et al, 2007a;Charbuillet et al, 2007b). With a similar goal in mind, in (Vignolo et al, 2006) an optimization strategy was also introduced in order to find an optimal wavelet packet decomposition.…”

Section: Introductionmentioning

confidence: 99%

Genetic Optimization of Cepstrum Filterbank for Phoneme Classification

Vignolo¹,

Rufiner²,

Milone³

et al. 2009

Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing

View full text Add to dashboard Cite

Some of the most commonly used speech representations, such as mel-frequency cepstral coefficients, incorporate biologically inspired characteristics into artificial systems. Recent advances have been introduced modifying the shape and distribution of the traditional perceptually scaled filterbank, commonly used for feature extraction. Some alternatives to the classic mel scaled filterbank have been proposed, improving the phoneme recognition performance in adverse conditions. In this work we propose an evolutionary strategy as a way to find an optimal filterbank. Filter parameters such as the central and side frequencies are optimized. A hidden Markov model classifier is used for the evaluation of the fitness for each possible solution. Experiments where conducted using a set of phonemes taken from the TIMIT database with different additive noise levels. Classification results show that the method accomplishes the task of finding an optimized filterbank for phoneme recognition.

show abstract

“…For the same purpose, the use of Linear Discriminant Analysis in order to optimise a filterbank has been studied in [11]. In other works the use of evolutive algorithms have been proposed to evolve features for the task of speaker verification [12,13]. Similarly, in [14] an evolutive strategy was introduced in order to find an optimal wavelet packet decomposition.…”

Section: Introductionmentioning

confidence: 99%