Okko in Speech

Nuance yesterday announced the acquisition of speech-related patents from IBM. The deal encompasses a “licensing and technical services agreement”, with IBM continuing to support existing customers. Integrated solutions of the two companies’ technologies are expected in two years time, according to the press release.

This deal represents a further step in market consolidation, which Nuance has pursued via a number of mergers and acquisitions over the past years. Friends in the industry tell me IBM has been trying to market their suite of IVR voice application server software more aggressively, however speech research activity, once part of the company’s “pervasive computing” vision, has declined lately.

Perhaps the IBM vision will bear fruit at Nuance, as the announcement comes with a commitment ” to proliferate advanced speech capabilities across a broad range of devices and environments”. One thing is sure: much like Nuance’s recent acquisition of Philips voice products, years after taking over Philips IVR products and solutions, this deal represents another closure, as Nuance has been marketing and supporting IBM’s ViaVoice product line for years. The de facto number of competitors on the speech and voice technology market is shrinking, as applications become more mainstream.

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IBM’s annual crystal ball list of Innovations That Will Change Our Lives in the Next Five Years includes a forecast of a voice-enabled talking web. “You will be able to sort through the Web verbally to find what you are looking for and have the information read back to you,” the article predicts.
IBM itself has launched several voice-enabled products and initiatives over the years, most notably the WebSphere Voice family of web servers, which adds various voice functionality to its flagship WebSphere platform, leveraging it in areas such as unified messaging and call-center automation.
Some problems exist with a vision as the one advocated by the article. Speech recognition accuracy and noise filtering have obviously come a long way and may only pose a minor impediment. The user’s desire to speak rather than type or click is another problem. Issuing voice commands in the presence of others may not always be desirable and can be disruptive, for instance at work on public transport. Lastly, there are usability concerns, beyond the quality of speech technology, when converting a visual 2- or even 3-dimensional representation of information into a 1-dimensional audio stream. The cognitive load increases significantly with tasks more complex than, for instance, obtaining time-table information or finding the nearest Italian restaurant.
The effort that stands behind the vision, to put voice technology to uses beyond call-center automation, is laudable. Mobile internet access and computing on-the-road may indeed do their parts to make this vision come true. And clearly, there are use cases, such as improved accessibility for users with impairments, that on their own accord merit making the web voice-accessible. Wide-spread usage of a voice-enabled web, however, may be more than five years off.

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!

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.