公开(公告)号：US5715368A

公开(公告)日：1998-02-03

申请号：US495155

申请日：1995-06-27

Applicant: Takashi Saito ,  Masaaki Okochi

Inventor： Takashi Saito ,  Masaaki Okochi

IPC: G06F17/27 ,  G10L13/06 ,  G10L13/08 ,  G10L5/04

CPC classification number: G10L13/10 ,  G10L13/08

Abstract: To synthesize speech, which is clear and high in naturalness, in a Japanese-language speech synthesis system by improving not only phoneme information but also rhythm information. In the Japanese-language, the independent word speech and the adjunct word speech are remarkably different in speech characteristic. The difference in speech characteristics between them is clearly observed, particularly in rhythmical elements such as the intensity, speech, and pitch of speech. From this fact, there is provided a new rule synthesis method which uses as a speech synthesis unit an adjunct word chain unit comprising a chain of one or more adjunct words and which is capable of synthesizing speech whose naturalness is high. The portion other than the adjunct word portion, i.e., the independent word portion, is constituted in a CV/VC unit.

Abstract translation: 通过不仅提高音素信息而且改善节奏信息，在日语语音合成系统中合成语音清晰自然的语音。 在日语中，独立词语和辅助词语言在语言特征上有显着差异。 明确地观察到它们之间的语言特征的差异，特别是在诸如强度，言语和言语间的节奏元素中。 从这个事实，提供了一种新的规则合成方法，其使用包括一个或多个附加词的链的附加字链单元作为语音合成单元，并且能够合成自然度高的语音。 除了附加字部分之外的部分，即独立字部分，以CV / VC单元构成。

公开(公告)号：US5502791A

公开(公告)日：1996-03-26

申请号：US114709

申请日：1993-09-01

Applicant: Masafumi Nishimura ,  Masaaki Okochi

Inventor： Masafumi Nishimura ,  Masaaki Okochi

IPC: G10L15/06 ,  G10L15/02 ,  G10L15/14 ,  G10L15/18 ,  G10L9/00

CPC classification number: G10L15/142 ,  G10L15/187 ,  G10L15/197 ,  G10L2015/0631

Abstract: Analysis of a word input from a speech input device 1 for its features is made by a feature extractor 4 to obtain a feature vector sequence corresponding to said word, or to obtain a label sequence by applying a further transformation in a labeler 8. Fenonic hidden Markov models for speech transformation candidates are combined with N-gram probabilities (where N is all integer greater than or equal to 2) to produce models of words. The recognizer determines the probability that the speech model composed for each candidate word would output the label sequence or feature vector sequence input as speech, and outputs the candidate word corresponding to the speech model having the highest probability to a display 19.

Abstract translation: 通过特征提取器4对来自语音输入装置1的字的输入进行分析，以获得与所述单词对应的特征向量序列，或通过在标签器8中应用进一步变换来获得标签序列。Fenonic hidden 用于语音变换候选的马尔科夫模型与N-gram概率（其中N大于或等于2的整数）组合以产生词的模型。 识别器确定为每个候选词组成的语音模型将作为语音输入的标签序列或特征向量序列输出的概率，并将与具有最高概率的语音模型相对应的候选词输出到显示器19。