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Acoustic change detection for robust automatic speech recognition based on a variance between distance dependent GMM models
Acoustic change detection for robust automatic speech recognition based on a variance between distance dependent GMM models
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机译：基于距离相关的GMM模型之间的差异的稳健自动语音识别的声学变化检测
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摘要
Acoustic change is detected by a method including preparing a first Gaussian Mixture Model (GMM) trained with first audio data of first speech sound from a speaker at a first distance from an audio interface and a second GMM generated from the first GMM using second audio data of second speech sound from the speaker at a second distance from the audio interface; calculating a first output of the first GMM and a second output of the second GMM by inputting obtained third audio data into the first GMM and the second GMM; and transmitting a notification in response to determining at least that a difference between the first output and the second output exceeds a threshold. Each Gaussian distribution of the second GMM has a mean obtained by shifting a mean of a corresponding Gaussian distribution of the first GMM by a common channel bias.
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