TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Speaker Adaptation Using i-Vector Based Clustering |
| 영문제목(English Title) |
Speaker Adaptation Using i-Vector Based Clustering |
| 저자(Author) |
Rui Liang
Hanwen Guo
Jiayu Liu
Ziyang Liu
Minsoo Kim
Gil-Jin Jang
Ji-Hwan Kim
Minho Lee
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| 원문수록처(Citation) |
VOL 14 NO. 07 PP. 2785 ~ 2799 (2020. 07) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
We propose a novel speaker adaptation method using acoustic model clustering. The similarity of different speakers is defined by the cosine distance between their i-vectors (intermediate vectors), and various efficient clustering algorithms are applied to obtain a number of speaker subsets with different characteristics. The speaker-independent model is then retrained with the training data of the individual speaker subsets grouped by the clustering results, and an unknown speech is recognized by the retrained model of the closest cluster. The proposed method is applied to a large-scale speech recognition system implemented by a hybrid hidden Markov model and deep neural network framework. An experiment was conducted to evaluate the word error rates using Resource Management database. When the proposed speaker adaptation method using i-vector based clustering was applied, the performance, as compared to that of the conventional speaker-independent speech recognition model, was improved relatively by as much as 12.2% for the conventional fully neural network, and by as much as 10.5% for the bidirectional long short-term memory
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| 키워드(Keyword) |
Scenic Image
Big Data
Ancient city
Content Analysis Method
Social Network Analysis
Entropy Weight Method.
Speaker adaptation
speech recognition
i-vector
clustering
hybrid HMM-DNN
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