Luckily I came across some useful reviews of the most prominent providers to get me up to snuff. I won’t go into them, as I’m sure others have more to say about the actual user experience. However as “mobile” is the way speech and langauge technology seems to want to go, and as I finally plan to use more personal mobile computing resources (especially various gadgets starting with “i”) for speech technology, I may give some of these a whirl in the near future…

SpinVox caused somewhat of a stir when launching their voice-to-text service in 2004 and another when the BBC “uncovered” that the company used a combination of human and machine intelligence. To anyone working in speech and language technology this would have been obvious from the get-go, as well as to anyone reading the company’s patent or patent applications, in which the use of human operators is mentioned explicitly. However regular users would probably have been duped into thinking a machine was doing all the typing.  Failure to understand/communicate this caused a wholly avoidable privacy debacle.

One thing that’s clear from last years privacy debacle is that there’s a bit of mess of terminology when it comes to voice and speech technologies.  So here’s an attempt at shedding some light on what’s what:

Speech Recognition – also ASR (automatic speech recognition) for short. This is the general term used to refer to the technology that automatically turns spoken words into machine-readable text. However there are different dimensions to describe this technology, such as models employed (HMM-based vs connectionist), who it’s for  (one single speaker or all speakers of a dialect or language).  Also, there is a host of applications that employ it (dictation, IVR/telephone systems, voice-to-text services), each with different requirements. Hence ASR is really an umbrella term.

Voice Recognition – often confused with speech recognition.  Usually voice recognition refers to software that works for only a single speaker.  However this is anecdotal and in marketing the two are used synonymously.

Voice-to-Text – a service that converts spoken words into text. Some ASR may be used to help to do so, as well as human transcribers, however the label itself makes no claim as to whether the process is fully automated.

Speaker Recognition – this is a security technology typically used to perform one of two tasks: (1) identifying a speaker from a group of known speakers or (2) determining whether a speaker is really who s/he claims. These are very similar tasks that people often confuse.  Think of the first one as picking a person out of a crowd and the second as a kind of “voice fingerprint matching”.

Text-to-Speech – or short TTS, another term for speech synthesis.  This technology is used to turn written text into an audio signal (such as an MP3).  This should be an obvious label, but surprisingly people seem to confuse it with Voice-to-Text services frequently (purely my own anecdote).

I’m also told SpinVox’s sales price of $102m is a bit of a disappointment, representing just over 50% of the initial $200m that SpinVox raised in 2003. But that’s something I’ll let others address. Let’s see where Nuance goes with this, in terms of trying to fully automate the whole transcription process…

http://translate.google.com/translate_tts?q=Hello+reader

Just replace “Hello+reader” with any text that you want spoken in your address bar.  Remember to replace spaces with pluses (+).

Some browsers however seem to have problems with the returned audio.  Chrome worked for me, though Internet Explorer is reportedly working as well.

As this is not an official RESTful Google API don’t be surprised if it stops working. Beware that commercial reuse of the output audio is likely also governed by license restrictions.

Update:
Friend Schamai pointed out how this could be employed in a web form. Here’s an example:

Or the corresponding HTML:

<form action="http://translate.google.com/translate_tts">
<input name="q" size="55" value="just saying" />
</form>


Also, here are some more or less recent, interesting news for Android (at about 6:20, thanks Schamai) and iPhone speech developers.

While not the only vendor to provide voice search (i.e. Yahoo oneSearch powered by Vlingo) Google certainly seems ahead in the game in what appears to be a gradual unfolding of a broad voice strategy, such as Voice Search and recently rebranding a feature-enhanced GrandCentral as Google Voice. Future work on the voice front we can expect includes promotion of its own speech recognition capacities through Android, Google Gears bringing speech capacities to all browers, tighter integration of Gaudi (audio indexing) with other services and perhaps one day opening up voice services over APIs.

As I’ve previously pointed out, to Google voice is just another form of data, but what’s slowly beginning to emerge is a central role for speech and voice technologies to play in coming developments for the web and how we search and interface with it.

While not a full product yet, this sounds like a nifty little application for cognitive off-loading.

Have you tried Microsoft Recite?

Aside from GOOG411, during the US presidential Google released Gaudi, a voice indexing technology for video. That makes the iPhone app the third official service the company releases, making use of speech recognition, leaving one guessing when Google’s speech technology becomes available as API, like the Google AJAX Language API for translation and transliteration, rather than bundled as software services. Also, an Android version is probably in the works, one would guess.

All applications are available in US English for now.

Google GAudi allows searching for keywords/phrases in the audio-stream of selected YouTube videos. Matches are represented as yellow slots on the playback slider. Top results appear as snippets of text from the audio surrounding the search term as well as information how many minutes into the video the term occurred.

The video material chosen to showcase GAudi is material concerning this year’s US presendential elections as “part of a broader effort around politics”, but also because of the high performance with such material and the relevance to testers and users.

Indexing does not appear to be complete, as using randomly chosen text fragments from showcased videos did not always result in a match. Google does say Gaudi is using its own speech recognition engine, perhaps the same employed by GOOG411, though most FAQs about technical details and how one could use GAudi for video are directed to email inquiries.

While GAudi is showcasing campaign material, it seems only a matter of time before audio indexing will be available for serving ad content on video.

In light of the browser war raging, a little add-on for Microsoft’s Live Messenger may not stir many waters, even if it promises real-time chat translation between English and 14 other languages. However it is still refreshing to read about technology, which is geared at opening channels of communication, rather than capturing market shares.

What are Google’s plans with Chrome and Android viz. Microsoft IE on Windows Mobile? Will Microsoft leverage its non-browser language services such as translation and speech recognition like Google has been?

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!