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首页> 外文期刊>WSEAS Transactions on Signal Processing >Detection of Speaker Identities from Cochannel Speech Signal
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Detection of Speaker Identities from Cochannel Speech Signal

机译：从Cochannel语音信号检测扬声器标识

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摘要

Supervised speech segregation for cochannel speech signal can be made easier if we use predetermined speaker's models instead of taking models for all the population. Here we propose a signal to signal ratio (SSR) independent method to detect speaker identities from a cochannel speech signal with unique speaker specific features for speaker identification. Proposed Kekre's Transform Cepstral Coefficient (KTCC)features are the robust acoustic features for speaker identification. A text independent speaker identification system is utilized for identifying speakers in short segments of test signal. Gaussian mixture modeling (GMM)classifier is used for the identification task. We compare the proposed method with a system utilizing conventional features called Mel Frequency Cepstral Coefficient (MFCC) features. Spontaneous speech utterances from candidates are taken for experimentation instead of utterances that follow a command like structure with a unique grammatical structure and have a limited word list in speech separation challenge (SSC)corpus. Identification is performed on short segments of the cochannel mixture. Two Speakers who have been identified for most of segments of the cochannel mixture are selected as two speakers detected for the same cochannel mixture. Average speaker detection accuracy of 93.56% is achieved in case of two speaker cochannel mixture for of KTCC features. This method produces best results for cochannel speaker identification even being text independent. Speaker identification performance is also checked for various test segment lengths. KTCC features outperform in speaker identification task even the length of speech segment is very short.
机译：如果我们使用预定的扬声器的型号而不是为所有人口占用模型，则可以更轻松地使Cochannel语音信号进行监督语音隔离。在这里，我们提出了信号比（SSR）独立方法的信号，以检测来自Cochannel语音信号的扬声器标识，具有用于扬声器识别的独特扬声器特定功能。提出的Kekre的转化临时临床系数（KTCC）特征是扬声器识别的强大声学功能。文本独立扬声器识别系统用于识别测试信号短段中的扬声器。高斯混合建模（GMM）分类器用于识别任务。我们将所提出的方法与利用传统特征的系统进行比较，该系统具有称为MEL频率谱系数系数（MFCC）特征的传统特征。来自候选人的自发言语是针对实验而不是遵循具有唯一语法结构的命令的语言，而是在语音分离挑战（SSC）语料库中有限的单词列表。在Cochannel混合物的短片段上进行识别。已经为大多数Cochannel混合物段识别的两个扬声器被选为检测同一Cochannel混合物的两个扬声器。对于KTCC特征的两个扬声器Cochannel混合物，实现了93.56％的平均扬声器检测精度。这种方法为Cochannel扬声器识别产生最佳结果，甚至是文本独立的。还检查了扬声器识别性能的各种测试段长度。 KTCC功能在扬声器识别任务中表达概率，即使语音段的长度也很短。

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