基于本征音子說話人子空間的說話人自適應算法

屈丹; 張文林

doi:10.11999/JEIT141264

基于本征音子說話人子空間的說話人自適應算法

doi: 10.11999/JEIT141264

屈丹¹,
張文林¹

基金項目:

國家自然科學基金(61175017, 61302107和61403415)資助課題

計量
- 文章訪問數(shù): 1273
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2014-09-30
- 修回日期: 2014-12-29
- 刊出日期: 2015-06-19

Speaker Adaptation Method Based on Eigenphone Speaker Subspace for Speech Recognition

Qu Dan¹,
Zhang Wen-lin¹

摘要

摘要: 本征音子說話人自適應算法在自適應數(shù)據(jù)量充足時可以取得很好的自適應效果，但在自適應數(shù)據(jù)量不足時會出現(xiàn)嚴重的過擬合現(xiàn)象。為此該文提出一種基于本征音子說話人子空間的說話人自適應算法來克服這一問題。首先給出基于隱馬爾可夫模型-高斯混合模型(HMM-GMM)的語音識別系統(tǒng)中本征音子說話人自適應的基本原理。其次通過引入說話人子空間對不同說話人的本征音子矩陣間的相關(guān)性信息進行建模；然后通過估計說話人相關(guān)坐標矢量得到一種新的本征音子說話人子空間自適應算法。最后將本征音子說話人子空間自適應算法與傳統(tǒng)說話人子空間自適應算法進行了對比?；谖④浾Z料庫的漢語連續(xù)語音識別實驗表明，與本征音子說話人自適應算法相比，該算法在自適應數(shù)據(jù)量極少時能大幅提升性能，較好地克服過擬合現(xiàn)象。與本征音自適應算法相比，該算法以較小的性能犧牲代價獲得了更低的空間復雜度而更具實用性。
- 語音信號處理 /
- 說話人自適應 /
- 本征音子 /
- 本征音子說話人子空間 /
- 低秩約束 /
- 本征音
Abstract: The eigenphone speaker adaptation method performs well when the amount of adaptation data is sufficient. However, it suffers from severe over-fitting when insufficient amount of adaptation data is provided. A speaker adaptation method based on eigenphone speaker subspace is proposed to overcome this problem. Firstly, a brief overview of the eigenphone speaker adaptation method is presented in case of Hidden Markov Model-Gaussian Mixture Model (HMM-GMM) based speech recognition system. Secondly, speaker subspace is introduced to model the inter-speaker correlation information among different speakers eigenphones. Thirdly, a new speaker adaptation method based on eigenphone speaker subspace is derived from estimation of a speaker dependent coordinate vector for each speaker. Finally, a comparison between the new method and traditional speaker subspace based method is discussed in detail. Experimental results on a Mandarin Chinese continuous speech recognition task show that compared with original eigenphone speaker adaptation method, the performance of the eigenphone speaker subspace method can be improved significantly when insufficient amount of adaptation data is provided. Compared with eigenvoice method, eigenphone speaker subspace method can save a great amount of storage space only at the expense of minor performance degradation.
- Speech signal processing /
- Speaker adaptation /
- Eigenphone /
- Eigenphones speaker subspace /
- Low-rank constraint /
- Eigenvoice

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參考文獻(16)

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