广播新闻中的语言自动识别

G. Backfried, R. Rainoldi, J. Riedler
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引用次数: 2

摘要

提出了广播新闻领域的自动语言识别实验。由于新闻广播固有的多样性,语音是从原始音频数据中提取出来的,通过电话级解码,使用广泛的音素类别。培训和测试是对来自不同欧洲电视频道的德语、英语、西班牙语和法语新闻节目的录音进行的。每种语言的特征都是由相应的声学特征单独创建的高斯混合模型。语音段的总体平均错误率为16.32%。目前的系统忽略了(几乎)任何类型的语言信息;然而,它因此很容易扩展到新的语言。
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Automatic language identification in broadcast news
We present experiments on automatic language identification in the broadcast news domain. Because of the inherent diversity of news broadcasts, speech is extracted from the raw audio data by means of phone-level decoding using broad classes of phonemes. Training and testing was performed on recordings of German, English, Spanish and French news shows from a variety of European TV channels. Each language is characterized by a Gaussian mixture model solely created from corresponding acoustic features. The overall average error rate on speech segments is 16.32%. The current system disregards (almost) any kind of linguistic information; however, it is therefore easily extensible to new languages.
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