无线电通信技术
無線電通信技術
무선전통신기술
Radio Communications Technology
2015年
6期
41-45
,共5页
陈雷%杨俊安%王龙%李晋徽
陳雷%楊俊安%王龍%李晉徽
진뢰%양준안%왕룡%리진휘
连续语音识别%瓶颈深度置信网络%区分性训练%ODLR
連續語音識彆%瓶頸深度置信網絡%區分性訓練%ODLR
련속어음식별%병경심도치신망락%구분성훈련%ODLR
Continuous Speech Recognition%Bottleneck Deep Belief Network%Discriminative Training%ODLR
大词汇量连续语音识别系统中,为了进一步增强网络的鲁棒性、提升深度置信网络的识别准确率,提出一种基于区分性和ODLR自适应瓶颈深度置信网络的特征提取方法。该方法首先使用鲁棒性较强的瓶颈深度置信网络进行初步特征提取,进而进行区分性训练,使网络的区分性更强、识别准确率更高,在此基础上引入说话人自适应技术对网络进行调整,提高模型的鲁棒性。利用提出的声学特征在多个噪声较强、主题风格较为随意的多个公共连续语音数据库上进行了测试,识别结果取得了22.2%的提升。实验结果表明所提出的特征提取方法有效性。
大詞彙量連續語音識彆繫統中,為瞭進一步增彊網絡的魯棒性、提升深度置信網絡的識彆準確率,提齣一種基于區分性和ODLR自適應瓶頸深度置信網絡的特徵提取方法。該方法首先使用魯棒性較彊的瓶頸深度置信網絡進行初步特徵提取,進而進行區分性訓練,使網絡的區分性更彊、識彆準確率更高,在此基礎上引入說話人自適應技術對網絡進行調整,提高模型的魯棒性。利用提齣的聲學特徵在多箇譟聲較彊、主題風格較為隨意的多箇公共連續語音數據庫上進行瞭測試,識彆結果取得瞭22.2%的提升。實驗結果錶明所提齣的特徵提取方法有效性。
대사회량련속어음식별계통중,위료진일보증강망락적로봉성、제승심도치신망락적식별준학솔,제출일충기우구분성화ODLR자괄응병경심도치신망락적특정제취방법。해방법수선사용로봉성교강적병경심도치신망락진행초보특정제취,진이진행구분성훈련,사망락적구분성경강、식별준학솔경고,재차기출상인입설화인자괄응기술대망락진행조정,제고모형적로봉성。이용제출적성학특정재다개조성교강、주제풍격교위수의적다개공공련속어음수거고상진행료측시,식별결과취득료22.2%적제승。실험결과표명소제출적특정제취방법유효성。
In order to further improve the robustness and recognition rate of deep belief network in Large Vocabulary Continuous Speech Recognition system,this paper presented a novel bottleneck deep belief network to extract new features, which was based on speaker adaptation and discriminative training.Firstly,a bottleneck deep belief network was adopted to get the feature.And discriminative training performed on this basis gave a more distinguished network to improve the recognition accuracy.Simultaneously,a more robust speaker adaptation method was introduced to adjust the network. The proposed method was tested on several public continuous speech databases with strong noise and casual themes and a relative 6.9% promotion of the recognition accuracy was obtained.The result proves the superiority of the proposed method compared to the conventional one.