In this paper, we present a way to distinguish human voice from animal voice. In deep learning-based solutions, we adopted one class classification called DOC and then two class classification called VoVNET to verify all kinds of animal voice and human voice collected on the Internet. We construct a device to react only human voice, starting the voice recognition engine. It has a superior potential when used in a interactive AI in terms of economy. In the result of our study, the accuracy reached about 95% in DOC and over 98% in VoVNET.
"},{"@language":"ja","@value":"この論文では、人の声と動物の声を聴き分ける方法について提案する。深層学習の手法のうち、提案手法１は 1 クラス分類の問題として、提案手法２の問題は 2 クラス分類として検証を行う。様々な動物の声をインターネット上から抽出し、また人の声についてもコーパスとして公開されている物を中心に抽出する。音声認識エンジンの利用開始時を特定するために人の声にのみ反応する学習機を構築することで、経済的側面からも対話型 AI を利用する際には優位に進められることが想像できる。研究成果として、1クラス分類での精度は 92%に達し、2 クラス分類では 98％に達した。
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