越小的孩子越能理解動物叫聲的含義

It may sound barking, but 10-year-olds can understand dogs better than people of any other age.
　　也許在我們聽來只是狗叫的聲音，在一個十歲的孩子看來卻不是，他能更好地領會狗狗說話的含義。

Researchers at Eo?tvo?s Lora?nd University in Budapest found that humans understand a dog’s bark from an early age, but that after 10, are not able to decipher meanings so easily.

在布達佩斯的一所大學中，研究人員發現孩子越小越能理解狗狗說話的含義，但一旦到了10歲，他們就不能那樣容易的理解了。

In tests, volunteers found it easiest to distinguish when a dog was angry – but 10-year-olds excelled at interpreting more subtle noises.

實驗中，志願者們都說區分狗狗是否在生氣是最容易的，但是10歲左右的孩子更擅長區分狗狗發出來的更細小的聲音。

The study’s results came from playing recordings of various bark ‘modes’ - such as warning off a stranger, playing and feeling lonely - to children aged six, eight and 10, and adults, and asking them to pair the noises with corresponding human facial expressions.

實驗是通過播放不同的狗叫聲來讓人辨認的，例如對陌生人的警告，或是獨自玩耍的孤獨，將這些聲音播放出來後，讓六歲，八歲，十歲的孩子及成人把這些聲音與相對應的人類表情歸類。

The authors, Pe?ter Pongra?cz and Csaba Molnár, said: ‘This shows that the ability of understanding basic inner states of dogs on the basis of acoustic signals is present in humans from a very young age.

研究者們說，“實驗說明，在人類非常年輕的時候，他們依據聽覺信號似乎更能理解狗狗的意思。”

'These results are in sharp contrast with other reports in the literature which showed that young children tend to misinterpret canine visual signals.’

“這個實驗結果和之前的說小孩子會誤解犬類視覺信號的實驗形成了鮮明對比。”

Molnár's other research in the field includes using machine-learning algorithms in an effort to further understand how humans 'listen' to dog barks.

他們的在該領域的其他實驗，例如利用機器學習算法來更深入瞭解人們是怎樣聆聽狗叫的。

Molnár and colleagues’ tested a computer algorithm’s ability to identify and differentiate the acoustic features of dog barks, and classify them according to different contexts and individual dogs.

研究人員利用計算機算法鑑別和區分的能力的聲學特徵的狗叫，並根據狗的不同種類將他們分類。

In the first experiment looking at classification of barks into different situations, the software correctly classified the barks in 43 per cent of cases.

在第一個實驗中，他們將狗叫根據不同情況進行分類，這個軟件的準確率高達43%。

In the second experiment looking at the recognition of individual dogs, the algorithm correctly classified the barks in 52 per cent of cases.

在第二個實驗中，是根據狗的不同種類進行區分，這次，該軟件的準確率達到了52%。

The software could reliably discriminate among individual dogs while humans cannot, which suggests that there are individual differences in barks of dogs even though humans are not able to recognise them.

該軟件能夠可靠的辨別人類不能辨別的不同種類的狗，這說明，不同的狗叫聲是存在不同的，儘管人類似乎不能分辨出來。

The authors concluded: ‘The use of advanced machine learning algorithms to classify and analyse animal sounds opens new perspectives for the understanding of animal communication. The promising results obtained strongly suggest that advanced machine learning approaches deserve to be considered as a new relevant tool for studying animal behaviour.’

研究人員總結說，“這種分類和分析動物聲音的高級機器爲人類理解動物之間的溝通打開了新局面。這項非常有成效的結果強有力的證明了，如此先進的機器學習法應該被視爲研究動物行爲的一種新的相關的工具。”