模式识别与人工智能
模式識彆與人工智能
모식식별여인공지능
Moshi Shibie yu Rengong Zhineng
2013年
3期
254-259
,共6页
说话人转换%话者无关模型%高斯混合模型%话者自适应训练
說話人轉換%話者無關模型%高斯混閤模型%話者自適應訓練
설화인전환%화자무관모형%고사혼합모형%화자자괄응훈련
Voice Conversion%Speaker Independent Model%Gaussian Mixture Model%Speaker Adaptive Training
提出一种基于话者无关模型的说话人转换方法.考虑到音素信息共同存在于所有说话人的语音中,假设存在一个可以用高斯混合模型来描述的话者无关空间,且可用分段线性变换来描述该空间到各说话人相关空间之间的映射关系.在一个多说话人的数据库上,用话者自适应训练算法来训练模型,并在转换阶段使用源目标说话人空间到话者无关空间的变换关系来构造源与目标之间的特征变换关系,快速、灵活的构造说话人转换系统.通过主观测听实验来验证该算法相对于传统的基于话者相关模型方法的优点.
提齣一種基于話者無關模型的說話人轉換方法.攷慮到音素信息共同存在于所有說話人的語音中,假設存在一箇可以用高斯混閤模型來描述的話者無關空間,且可用分段線性變換來描述該空間到各說話人相關空間之間的映射關繫.在一箇多說話人的數據庫上,用話者自適應訓練算法來訓練模型,併在轉換階段使用源目標說話人空間到話者無關空間的變換關繫來構造源與目標之間的特徵變換關繫,快速、靈活的構造說話人轉換繫統.通過主觀測聽實驗來驗證該算法相對于傳統的基于話者相關模型方法的優點.
제출일충기우화자무관모형적설화인전환방법.고필도음소신식공동존재우소유설화인적어음중,가설존재일개가이용고사혼합모형래묘술적화자무관공간,차가용분단선성변환래묘술해공간도각설화인상관공간지간적영사관계.재일개다설화인적수거고상,용화자자괄응훈련산법래훈련모형,병재전환계단사용원목표설화인공간도화자무관공간적변환관계래구조원여목표지간적특정변환관계,쾌속、령활적구조설화인전환계통.통과주관측은실험래험증해산법상대우전통적기우화자상관모형방법적우점.
@@@@A voice conversion method based on speaker independent (SI) model is proposed. Considering the phoneme information that commonly exists in every speaker's speech, an SI space described only by the phoneme information is assumed to exist. Gaussian mixture model ( GMM) is adopted to model the distribution of the SI space, and the mapping relations from speaker dependent (SD) space to SI space are described by linear transformations. The SI model is trained by using speaker adaptive training (SAT) algorithm on a multi-speaker database. In the conversion phase, the conversion function from source space to target space is quickly and flexibly built by joining the transformations from source space to SI space and SI space to target space. The advantage of the proposed method is proved by the results of some listening tests compared with two representative conventional methods.
