Okko in Speech

The speech application industry got some critical press in recent months (here are some spirited responses, respectively.)

All the more refreshing to come across this New York Times article presenting current work in speech and artificial intelligence. The article highlights broadly what kind of AI applications have moved into the mainstream (or have potential to do so). Speech and natural language understanding, the article claims, have gone furthest.

One thing that is generalizable from both criticisms above is that development of speech-enabled applications has stagnated, in various ways¹. The underlying technology – speech recognition (ASR) – has gone as far as it can. Application designers and developers haven’t adopted. Dictation has learned to understand doctors and lawyers better, but still struggles with conversational speech.

This point may have to be conceded. In terms of commercial applications however, especially speech-enabled voice (IVR) systems, the root cause for stagnation is not necessarily a failure of AI, rather than a maturing of standards and best-practices. Fulfilling expectations that voice applications, much like websites, behave according to certain rules is much to the advantage of the millions who interact with such systems every day.

What I walk away with from the generalized critical, as well as the Times’ optimistic perspective is that, short of a revolution in underlying technologies (which hardly anyone expects), filling practical, everyday niches is where things can still move forward for speech and language processing.  These niches have certainly not been fully uncovered.

Thoughts?

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¹ Roughly summarized, Robert Fostner: “development in speech technology has flat-lined since 2001″; David Suendermann: “(statistical) engineering methods are more efficient than traditional symbolic linguistic approaches to language processing.”

The old maxim “I need more data” should be familiar to anyone who has ever tried to wrestle with language technology issues, attempted speech application tuning or delved into any statistical approach to an AI-related problem. Google moved into the speech world last year with GOOG-411, a speech recognition driven directory assistance application (you say what you are looking for and where, it returns suitable businesses and connects you to the one you want or sends you details in an SMS).
Like all (well, most) other Google services, GOOG-411 is free for the end-user. As such, the basic business model (collect data, turn data into cash) applies. This was recently confirmed in interview by Marissa Mayer, Google’s VP of Search Products and User Experience:

Whether or not free-411 is a profitable business unto itself is yet to be seen. I myself am somewhat skeptical. The reason we really did it is because we need to build a great speech-to-text model … that we can use for all kinds of different things, including video search.

Google thus couples statistical AI and its general data-driven approach to everything in a novel way. In doing so, Google may find itself in a catch-up race with the ilk of Nuance, Loquendo IBM, or Telisma, whose stronghold on speech recognition technology comes, in part, from having aggregated speech and language databases through data collection during professional services projects.
What’s new in Google’s approach, however, is the convergence of the dual role that data plays in AI and in the overall service-driven business model. Google will presumably not be content to bootstrap a pattern matching engine to sell licenses like the technology companies above. More interestingly to follow will be the range of services Google can spin using this technology (context sensitive video advertising, audio indexing, IVR hosting) which are more befitting of their overall company strategy.
Unsurprisingly, Mayer goes on to claim that Google isn’t working on ways out of the world of brute-force data-driven algorithms:

People should be able to ask questions, and we should understand their meaning, or they should be able to talk about things at a conceptual level. … A lot of people will turn to things like the semantic Web as a possible answer to that. But what we’re seeing actually is that with a lot of data, you ultimately see things that seem intelligent even though they’re done through brute force.

User privacy advocates may also have a thought or two on this new dimension of data collection, as Google is beginning to loose the “conventionally trustworthy” image it held amongst many over the past years. Fortunately the ways in which speech data is commonly used to train pattern matching models involves very little in the ways of privacy infringement.
Happy data collecting!

On the WWW today:

• Some recent news about Google and MS voice search offensive.
• Epson chip to feature Fonix DECtalk speech synthesis.

On the WWW today:

• Article providing roundup of some semantic web solutions.
• InQuira executive to discuss benefits of NLP for search engine optimization.

Today on the WWW:

• Software Ali Baba parses medical abstracts, generates visual network or terminology using natural language processing.
• A redux of latent semantic indexing (LSI) for use in search engines.

Web 3.0 is getting some buzz in the blogosphere. Like Web 2.0, it begs the question that PCMag.com recently ran by its readers: what is it? However this time around things seems a bit easier.

Web 2.0 seems to be happy with being vaguely defined (delimited may be a better term) and equally a social and a technological movement. Web 3.0 clearly hovers over the idea of the “Semantic Web”, a term coined by Tim Berners-Lee, in which richly mark-upped hypertext and data allow for novel more meaningful human-machine and machine-machine communication. Radar Networks (currently in stealth mode) claim to be driving some interesting developments in this direction and are followed closely by those interested.

This has already raised some questions: will content be expensive hand labor or machine boot-strappable, what new privacy policies do we have to live with, how does one separate style and content, what are alternatives to RDF.

Sadly, there’s very little inspiring out there about potential applications.

My question (though not uniquely mine) to add to this: What role will natural language processing play in this (i.e. how “semantic” is this talk of Semantics)? Semantic content in RDF appears to be little more than a means for one machine to tell another who authored a particular book or what are the postal codes in the greater Boston area. Semantics to me is as much about intentions (“Why is web-service A dispensing such information?”) and interpreting such information for the purposes of action (“What can web-service B – or my browser or I – do with it?”).

Perhaps this misses the mark and semantic really isn’t about natural language. But there is a weaker, more real form of this “language and technology” concern: Insofar as semantics is just information, can it be bootstrapped by a machine (perhaps even linguistically informed rather than statistically)?

Thoughts?

Daily News Redux:

• Some electronic dictionary news. Coolsoft Coolexon electronic dictionary features 60 languages, including translation and TTS accessibility features, is only 3.3MB large. Also in the news, Ultralingua and Collins partner to release electronic dictionaries.
• Some business automation success stories using speech recognition.
• Some more semantic search engine-related news featuring ZoomInfo and Cognition.
• Arab language speech recognition market growing.
• Wizzard announces 2006 financial results.

Questions of the day:

• Web X.0 IEEE workshop. What role will NLP play?
• Are GPS navigation systems driving the TTS market (links randomly chosen from recent navigation system releases)?

On the WWW today:

• CallMiner announces Eureka product for call center speech analytics and QA.
• Envox CT Connect 7 VXML/CTI plattform now Avaya telephony compliant
• Some blogging about the role of symbolic vs brute-force statistics in articificial intelligence, NLP, Google‘s machine translation vision.

On the WWW today:

• Article about Google statistical machine translation algorithms, mentions success in Arabic (cf. NIST benchmarks finding Google’s Arabic/Chinese->English translation most accuracte.)
• Teragram MyGAD.com search engine launch, employing NLP for improved information retrieval. In related news, a list of top-100 search engines, including more NLP and some audio searches.
• Article about predicive software application for the tourism industry, calls for NLP and other AI techniques such as neural networks.
• Nuance unveils voice music search application for mobile ASR applications. In related news, Nuance ships improved mobile TTS.

To start us off, recent experience has shown three things:

1. Speech (i.e. voice) related news is TTS-dominated, less so by ASR.
2. The company featured most frequently in the news is Nuance.
3. The talk of semantic search engines seems to dominate the NLP news.

The success of TTS is largely due to requirements set by mobile and in-car technologies, especially GPS and communications. The future of ASR in the other hand seems to depend on the dictation market (especially in the healthcare sector) and a growing relevance of network ASR (driven by advancing VoIP, impact of multi-modal applications).

Nuance’s continued position will depend on the role of “super players” IBM and Microsoft and to a lesser degree the role of open-source initiatives, especially on the network/telephony side.

Semantic search engines recently got some media hype with “Google-Killer” Powerset, a PARC offspring. While in its infancy, some believe this development towards semantic web will usher in a Web3.0 revolution. Of course, soem others believe this has already begun, while yet more just wanna see what happens with all this.

Let’s see how these trends develop. Especially multi-modality and semantic searches will be issues to follow closely.