This is the first in a series of posts I’ll do on speech application tuning over the coming weeks. Hopefully, this will provoke interesting feedback and, who knows, even spark some lively discussions.

I’m starting with the very important topic of speech recognition metrics because that’s necessary in order set the stage for most of what I’ll talk about next. Although this may not be the most exciting topic, it is clearly a very important one.

Some terminology

Tuning a speech application involves attempts to optimize a certain number of key performance metrics. Improvements or deterioration of these metrics is what tells us whether or not we’re making progress. Although that should be intuitively obvious to most people, what’s perhaps less obvious is how to select metrics that correlate best with the application’s success rate and user experience in the field.

Let me start by defining some terminology:

• In-grammar / out-of-grammar —This determines whether an utterance is covered or not by the grammar. Later on in this post, I’ll talk at length about the different ways the word “covered” may be interpreted.
• Accepted / rejected —This determines whether the recognition result’s confidence score is greater (accepted) or smaller (rejected) than the given confidence threshold. Note that there may be more than one confidence threshold for a given recognition context. If we’re talking about the high threshold, then “accepted” usually means that no confirmation is required. If we’re talking about the low threshold, then “accepted” means a confirmation will be required and “rejected” means that the user needs to be re-prompted.
• Correct / incorrect —This determines whether or not a recognition result is correct. Although the definition of “correct” may vary, it is often interpreted to mean that the top recognition result in the N-best list has the correct semantic result (i.e., it doesn’t matter that not all words were correctly recognized as long as the semantic result is correct). Note that we assume here that only in-grammar utterances can be classified as either correct or incorrect.

When we perform a recognition test for a grammar using utterances collected in the field, we compute a set of 6 counters for each confidence threshold value in a range from 0.0 to 1.0. These counters are:

• AC — Number of in-grammar utterances that are accepted and correct (often called CA-in in the industry)
• AI — Number of in-grammar utterances that are accepted and incorrect (often called FA-in)
• RC — Number of in-grammar utterances that are rejected and correct
• RI — Number of in-grammar utterances that are rejected and incorrect
• Aoog — Number of out-of-grammar utterances that are accepted (often called FA-out)
• Roog — Number of out-of-grammar utterances that are rejected (often called CR-out)

Note that the value FR-in, often seen in the industry, is equal to RC+RI. We like to keep these two values separate since they allow us to distinguish recognition errors from rejection errors. We add two important variables, that are computed from the above counters:

• ing — Number of in-grammar utterance (= AC+AI+RC+RI)
• oog — Number of out-of-grammar utterances (= Aoog+Roog)

With these, we define the two key metrics that we’ll use constantly:

• Correct accept rate (CA-rate) —This is the percentage of in-grammar utterances that are accepted with a correct result. It is computed as CA-rate = AC/ing.
• False accept rate (FA-rate) — We use two versions of this metric:
  1. The percentage of all utterances that are incorrectly accepted. It is computed as: FA-rate = (AI+Aoog)/(ing+oog) = (AI+Aoog)/all
  2. The percentage of accepted utterances that are incorrect. It is computed as FA-rate = (AI+Aoog)/(AC+AI) = (AI+Aoog)/A

Here’s an example that will hopefully help clarify all this. The following graph plots the CA-rate as a function of the FA-rate for a phone number recognition experiment. I’ll use this type of graph constantly, so you might want to familiarize yourself with it. Note that, in order to avoid any confusion, the axes are labeled with the metric’s definition, not its name.

In the graph, the hidden variable is the confidence threshold. As the confidence threshold decreases from 1.0 to 0.0, both the CA-rate and the FA-rate increase. If we are using two thresholds then we would want to set the high threshold so that the FA-rate is very low (less than 1%, say), while the low threshold would be set in order to have an appropriate balance between confirming too many incorrect results and rejecting too many correct results.

Using a graphical representation of results has several advantages. One advantage is that it provides a visually clear view of how effectively we avoid false accepts. A curve that grows slowly from left to right is a clear indication that we’re not effectively rejecting out-of-grammar utterances. We’d like the curve to initially have a very steep slope and then to taper off when the CA-rate gets close to the maximum value.

Another very important advantage is that it makes it easy to compare results from different experiments. A curve that’s above another immediately tells us that it’s a better result (for a given FA-rate, we have a better CA-rate). That, however, assumes that the results truly are really comparable, which brings us back to an issue that we had earlier postponed: The definition of “in-grammar”.

The importance of correctly defining “in-grammar”

I had mentioned earlier that an in-grammar utterance is an utterance that is “covered” by the grammar. But what does “covered” mean? For much of the industry, this simply means that the sentence transcription can be parsed by the grammar.

This definition turns out to be quite problematic. Fundamentally, the main problem is that the in-grammar utterances are those we consider valid and therefore those that we should recognize as best as possible while out-of-grammar utterances are those the application should be rejecting. However, the definition of “valid” should be based on what application users perceive, not on what we have decided that the grammar should cover. If, for speech recognition accuracy considerations, we decide not to cover certain forms of user responses that are not used very often, this doesn’t make them any less valid. It just makes them less frequent.

Let me illustrate this with a simple example. The graph below shows the results from three date recognition experiments. The blue curve shows the result obtained using a grammar that supports two main forms: <month><day>[<year>] (“January fifth”) and <day><month>[<year>] (“Fifth of January”). Let’s say we want to see what happens if we decide not to support the second, rarer form. The result is the red curve which, as we can see, seems to indicate better performance.

But that’s an illusion. The problem is that the red curve considers all dates of the form “Fifth of January” as out-of-grammar. We’re of course doing a better job of recognizing the now reduced set of in-grammar utterances, but the problem is that a larger proportion of user utterances are considered invalid. In other words, we’re now correctly handling a smaller proportion of valid user utterance. From the user’s perspective, that’s certainly not an improvement. In fact, if we consider both forms as in-grammar (i.e., valid), then we get the green curve, which clearly tells us that we indeed have considerably deteriorated results.

In order to get meaningful results, we need to determine which utterances are in-grammar and which are out-of-grammar based on what should be considered a valid response, regardless of what we decide to include in our grammar. This has several important benefits:

• It makes it possible to get meaningful comparisons between results obtained with grammars having different coverages since the sets of in-grammar and out-of-grammar utterances is the same in all cases.
• We get results that are much more representative of user experience. To make sure that this is the case, we normally decide that an utterance is “in-grammar” if a human listener would consider this to be valid and unambiguous response to the question (within an acceptable “domain” of responses).
• Last, but not least, we get applications that deliver similar or better success rates with fewer confirmations, and therefore better user experience. The reason is that, very often, many sentences that can’t be parsed by the grammar nonetheless give a high-confidence, correct semantic result. If these were considered out-of-grammar, then they would become false accepts. It takes very few of these to have a significant impact on the FA-rate, with the unfortunate consequence that we would end up using confidence thresholds much higher than necessary, resulting in many unnecessary confirmations.

So this pretty much concludes what I wanted to talk about today. In the next post, I’ll talk about tuning challenges related to out-of-grammar utterances.