An Effective Speaker Clustering Method using UBM and Ultra-Short Training Utterances

• Autorzy: Hossa R. , Makowski R.

Identyfikatory

DOI

10.1515/aoa-2016-0011

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

automatic speech recognition; interindividual difference compensation; speaker clustering; universal background model; GMM weighting factor adaptation

• Wydawca: Instytut Podstawowych Problemów Techniki PAN ; Komitet Akustyki PAN ; Polskie Towarzystwo Akustyczne
• Czasopismo: Archives of Acoustics
• Rocznik: 2016
• Tom: Vol. 41, No. 1
• Strony: 107--118
• Opis fizyczny: Bibliogr. 26 poz., rys., tab., wykr.

Twórcy

autor

Hossa R.

• Signal Processing Systems Department, Faculty of Electronics, Wroclaw University of Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland

autor

Makowski R.

• Signal Processing Systems Department, Faculty of Electronics, Wroclaw University of Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland

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Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.