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Wyszukujesz frazę "universal background model" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
An Effective Speaker Clustering Method using UBM and Ultra-Short Training Utterances
Autorzy:
Hossa, R.
Makowski, R.
Powiązania:
https://bibliotekanauki.pl/articles/176593.pdf
Data publikacji:
2016
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
automatic speech recognition
interindividual difference compensation
speaker clustering
universal background model
GMM weighting factor adaptation
Opis:
The same speech sounds (phones) produced by different speakers can sometimes exhibit significant differences. Therefore, it is essential to use algorithms compensating these differences in ASR systems. Speaker clustering is an attractive solution to the compensation problem, as it does not require long utterances or high computational effort at the recognition stage. The report proposes a clustering method based solely on adaptation of UBM model weights. This solution has turned out to be effective even when using a very short utterance. The obtained improvement of frame recognition quality measured by means of frame error rate is over 5%. It is noteworthy that this improvement concerns all vowels, even though the clustering discussed in this report was based only on the phoneme a. This indicates a strong correlation between the articulation of different vowels, which is probably related to the size of the vocal tract.
Źródło:
Archives of Acoustics; 2016, 41, 1; 107-118
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An improved ant colony optimization algorithm and its application to text-independent speaker verification system
Autorzy:
Aghdam, M. H.
Powiązania:
https://bibliotekanauki.pl/articles/91678.pdf
Data publikacji:
2012
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
ant colony
optimization
ant colony optimization
ACO
security
automatic speaker verification
ASV
feature space
Gaussian mixture model universal background model
GMM-UBM
Opis:
With the growing trend toward remote security verification procedures for telephone banking, biometric security measures and similar applications, automatic speaker verification (ASV) has received a lot of attention in recent years. The complexity of ASV system and its verification time depends on the number of feature vectors, their dimensionality, the complexity of the speaker models and the number of speakers. In this paper, we concentrate on optimizing dimensionality of feature space by selecting relevant features. At present there are several methods for feature selection in ASV systems. To improve performance of ASV system we present another method that is based on ant colony optimization (ACO) algorithm. After feature selection phase, feature vectors are applied to a Gaussian mixture model universal background model (GMM-UBM) which is a text-independent speaker verification model. The performance of proposed algorithm is compared to the performance of genetic algorithm on the task of feature selection in TIMIT corpora. The results of experiments indicate that with the optimized feature set, the performance of the ASV system is improved. Moreover, the speed of verification is significantly increased since by use of ACO, number of features is reduced over 80% which consequently decrease the complexity of our ASV system.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2012, 2, 4; 301-315
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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