Speech recognition

Analysis of technologies that recognize and/or respond directly to voice and speech. Related subjects include:

June 9, 2006

That great linguist, Groucho Marx, and other stories

If you’re reading this blog, you’re probably familiar with a saying that illustrates some of the basic challenges of disambiguation:

Time flies like an arrow. Fruit flies like a banana.

But did you know who said it first? I didn’t until recently. Read more

December 11, 2005

The text technologies market 3: Here’s what’s missing

The text technologies market should be booming, but actually is in disarray. How, then, do I think it should be fixed? I think the key problem can be summed up like this:

There’s a product category that is a key component of the technology, without which it won’t live up to nearly its potential benefits. But there’s widespread and justified concern over its commercial viability. Hence, the industry cowers in niches where it can indeed eke out some success despite products that fall far short of their true potential.

The product category I have in mind, for lack of a better name, is an ontology management system. No category of text technology can work really well without some kind of semantic understanding. Automated clustering is very important for informing this understanding in a cost-effective way, but such clustering is not a complete solution – hence the relative disappointment of Autonomy, the utter failure of Excite, and so on. Rather, there has to be some kind of concept ontology that can be use to inform disambiguation. It doesn’t matter whether the application category is search, text mining, command/control, or anything else; semantic disambiguation is almost always necessary for the most precise, user-satisfying results. Maybe it’s enough to have a thesaurus – i.e., a list of synonyms. Maybe it’s enough to define “concepts” by simple vectors of word likelihoods. But you have to have something, or your search results will be cluttered, your information retrieval won’t fetch what you want it to, your text mining will have wide error bars, and your free-speech understanders will come back with a whole lot of “I’m sorry; I didn’t understand that.”

This isn’t just my opinion. Look at Inquira. Look at text mining products from SPSS and many others. Look at Oracle’s original text indexing technology and also at its Triplehop acquisition. For that matter, look at Sybase’s AnswersAnywhere, in which the concept network is really just an object model, in the full running-application sense of “object.” Comparing text to some sort of thesaurus or concept representation is central to enterprise text technology applications (and increasingly to web search as well).

Could one “ontology management system,” whatever that is, service multiple types of text applications? Of course it could. The ideal ontology would consist mainly of four aspects:

1. A conceptual part that’s language-independent.
2. A general language-dependent part.
3. A sensitivity to different kinds of text – language is used differently when spoken, for instance, than it is in edited newspaper articles.
4. An enterprise-specific part. For example, a company has product names, and competitors with product names, and those names have abbreviations, and so on.

Relatively little of that is application-specific; for any given enterprise, a single ontology should meet most or all of its application needs.

Coming up: The legitimate barriers to the creation of an ontology management system market, and ideas about how to overcome them.

December 9, 2005

The text technologies market 2: It’s actually in disarray

The text technologies market should be huge and thriving. Actually, however, it’s in disarray. Multiple generations of enterprise search vendors have floundered, with the Autonomy/Verity merger being basically a combination of the weak. The RDBMS vendors came up with decent hybrid tabular/text offerings, and almost nobody cared. (Admittedly, part of the reason for that is that the best offering was Oracle’s, and Oracle almost always screws up its ancillary businesses. Email searchability has been ridiculously bad since — well, since the invention of email. And speech technology has floundered for decades, with most of the survivors now rolled into the new version of Nuance.

Commercial text mining is indeed booming, but not to an extent that erases the overall picture of gloom. It’s at most a several hundred million dollar business, and one that’s highly fragmented. For example, at a conference on IT in life sciences not that long ago, two things became evident. First, the text mining companies were making huge, intellectually fascinating, life-saving contributions to medical research. Second, more than ten vendors were divvying up what was only around a $10 million market.

If text technology is going to achieve the prominence and prosperity it deserves, something dramatic has to change.

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