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Speech variability in automatic speaker recognition systems for commercial and forensic purposes

机译:用于商业和司法目的的自动说话人识别系统中的语音可变性

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摘要

Speaker recognition is a major task when security applications through speech input are needed. Nevertheless, speech variability is a main degradation factor in speaker recognition tasks. Both intra-speaker and external variability sources produce mismatch between training and testing phases. In this contribution, channel and inter-session variability are explored in order to accomplish real automatic systems for both commercial and forensic speaker recognition. Results are presented making use of "AHUMADA", a subset of "GAUDI" large speaker recognition-oriented database in Spanish.
机译:当需要通过语音输入进行安全应用时,说话人识别是一项主要任务。但是,语音可变性是说话人识别任务中的主要降级因素。说话者内部和外部变异性源都会在训练和测试阶段之间产生不匹配。在此贡献中,探索了频道和会话间的可变性,以实现用于商业和法医说话人识别的真正的自动系统。使用“ AHUMADA”(西班牙语的“ GAUDI”大型面向说话者识别的数据库的子集)显示了结果。

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