狗狗的每一声“汪”，你听懂了吗？

译者 夏了了

It may sound barking, but 10-year-olds can understand dogs better than people of any other age.

       10岁的孩子比其他年龄段的人更能听得懂犬吠。

Researchers at Eötvös Loránd 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.

       在布达佩斯，罗兰大学的研究者们发现小朋友可以理解狗叫的含义，但是超过10岁，想听懂就不那么容易了。

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

       测试中，志愿者们发现当狗发怒的时候最容易听出来，但是十岁的孩子善于辨别更微妙的狗叫。

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.

       研究人员向六岁、十岁和成人志愿者播放不同“风格”的狗叫，如，吠阻陌生人态，玩耍态以及孤独态，然后让他们将听到的狗叫与相一致的人类表情配对，由此得到实验结果。

The authors, Péter Pongrácz 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.'

       作者Péter Pongrácz和Csaba Molnár说：“这个实验揭示出，这种理解狗狗通过基本声音信号所表达的内心情感的能力，是为年幼的人所具有的。”

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

       “这些研究成果与其他文献形成鲜明比照，那些报告表示幼童倾向于误解狗发出的视觉信号。”

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

       Molnár在这个领域的其他研究包括，使用人工智能来更进一步了解人类是如何“听”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.

       Molnár和同事测试了一个软件的效果，这个软件能够识别和区分不同狗叫的特征，然后根据不同的情境和不同的狗狗归类。

The software analysed more than 6,000 barks from 14 Hungarian sheepdogs (Mudi breed) in six different situations: ‘stranger’, ‘fight’, ‘walk’, ‘alone’, ‘ball’ and ‘play’.

       这个软件分析了14只匈牙利牧羊犬（马地犬）在六种不同的状况下的6000多次狗叫，这六种状况分别是：遭遇陌生人，打斗，散步，独处，同类相聚，玩耍。

The barks were recorded with a tape recorder before being transferred to the computer, where they were digitised and individual bark sounds were coded, classified and evaluated.

       研究人员先用磁带记录这些叫声，然后再通过计算机将其数字化、然后分类、评估。

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

       在首次进行狗叫辨识实验时，软件的成功率达到了43%。

The best recognition rates were achieved for ‘fight’ and ‘stranger’ contexts, and the poorest rate was achieved when categorizing ‘play’ barks.

       识别率最高的情境是“打斗”和“遭遇陌生人”，最低的则是“玩耍”。

These findings suggest that the different motivational states of dogs in aggressive, friendly or submissive contexts may result in acoustically different barks.

       这些研究表明，狗在不同的状态下，比如好斗态，友好态或顺从态，会发出不同的叫声。

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

       第二次狗叫辨识实验，该软件的正确率达到了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.

       软件能可靠地区别出不同的狗叫，而人类不行，这表明尽管人类分辨不出，但狗狗的叫声中其实是有差别的。

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.’

       作者总结道：“使用先进的人工智能技术把动物的声音归类分析，为研究动物的交流沟通打开了一片新天地。先进的人工智能技术由于其光明的前景，理应被看作研究动物行为模式的新工具。”