What exactly is hand labelling in Software Writer QR Code JIS X 0510 in Software What exactly is hand labelling

17.1.5 What exactly is hand labelling generate, create qr code iso/iec18004 none with software projects Oracle's Java An issue that is seldom add Software qrcode ressed on the issue of labelling is that of just what a labeller is doing when he or she hand labels some data. One sees the terms hand labelling and expert labeller quite frequently in this context, but what do these mean Here we take the position that there are really two types of labelling; intuitive and analytical. Intuitive labelling is where the human labeller makes a judgment using their own language ability, but without using any explicit reasoning based on this.

The homograph example above is a good case of this. If we present the sentence he plays bass guitar to someone and ask them which word bass corresponds to, they will quickly and certainly say that it is BASS - MUSIC. No knowledge of linguistics or any of the issues involved is required to make this judgment.

Now consider a case where someone is asked which ToBI pitch accent is used on a word in a spoken sentence. It is impossible to perform this task without specialist training; even most experienced linguists or speech engineers would have trouble with this unless they had direct experience in ToBI labelling itself. Our position is that intuitive labelling is nearly always useful and reliable, whereas analytical labelling is usually neither.

With intuitive labelling, we are in effect using a person s in built, natural linguistic ability to make a judgment. As most people are perfect speakers of their language, tapping into this knowledge gives access to a very powerful knowledge source. The tasks in TTS that come under this sort of labelling include homographs, sentence boundaries, semiotic classi cation and verbalisation.

Analytic labelling normally relies on the labeller applying a set of procedures or labelling rules to the data. Consider a case where a labeller is asked to decide whether a prosodic phrase break should occur between two words in a spoken sentence. To do this, the labeller must rst be instructed as to what a prosodic phrase break is.

Next they need to have an explanation of how to nd one, and this may consist of clues such as listen for lengthening at the ends of words , examine pauses and see if these constitute phrase breaks and so on. In many cases the labeller will apply these rules easily, but in a signi cant number of cases the labeller will be in doubt, and maybe further rules need be applied. It is here where the problem lies.

From considerable experience in labelling databases of all kinds and with all types of labels it is clear that there are a large number of problematic cases where the correct label is not immediately obvious. This can often lead to labellers making spot judgments, and this can often lead to considerable inconsistency in labelling, both between labellers and between different sessions of the same labeller. One of the main sources of dif culty in labelling is that the labelling model which provides the labels to be chosen is in some sense lacking.

As we saw in Section 9.3, there is an enormous range of intonation models and theories, and while we reserve judgment on which one is better than another, it is clear that they can t all be right. A more accurate statement is that they all have.

Section 17.1. Databases good aspects but none are c omplete or an accurate model of the reality of intonation. Hence in every labelling situation, in many cases the labeller is trying to force a square peg in a round hole, and dif culties will always ensue. The problem results in very poor labelling agreement.

As previously stated, good agreement in intonation labelling is considered to be 70% [406], which not only shows how poor the agreement is but also how low the expectations in the community that this can be considered good. Furthermore, experience has shown that the more complicated or dif cult the labelling system is, the longer this takes to perform, and the result may not only be an inaccurately labelled database but one that was time consuming and expensive to collect. This situation strongly contrasts with intuitive labelling where labellers usually make quick, effortless judgments with a high degree of accuracy.

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