Speech Wrecko

I apologize! That title was actually generated by Microsoft’s speech recognition system incorrectly transcribing “Microsoft’s 5.1% Word Error Rate (WER) Announcement is Completely Misleading”.   Okay, that was snarky, but I promise Microsoft compelled me to write that.  You see in the course of editing my previous post Microsoft had to go and put out a press release announcing “Microsoft Researchers Achieve new Conversational Speech Recognition Milestone”.  Their announcement flies in the face of my previous post and therefore I had no choice but to attempt an epic takedown.

Before I try to dismantle Microsoft’s irrational clam I would like to state that the the researchers at Microsoft (some of whom I have crossed paths with while working on the Xbox Kinect and HoloLens) have done some solid research with potential implications on how we build production speech recognition systems.   I have no issues with the technical nature of the research paper underpinning the press release, but I do take issue with the marketing and PR spin applied on top of it.  So without further ado “LET’S GET READY TO RUMBLE”.

There are two primary issues with the announcement made by Microsoft:

1. Does Microsoft’s testing provide conclusive evidence that the 5.1% WER results will generalize
2. Are the tactics used viable from a cost/compute/timeliness perspective in a production system

Let’s tackle each of these issues independently.

Will the Results Generalize

In my previous post I discussed why large data sets were critical for training truly accurate conversational speech recognition systems.   While I do take issue with the data size used to train the Microsoft speech recognition system, the larger issue is with the test set used to validate the word error rate.

In Andrew Ng’s seminal talk on the “nuts and bolts of machine learning”, he goes into great detail on the different data sets required for training, testing and validating machine learning algorithms.  I encourage anybody interested in the optimal process for training and testing machine learning / AI like algorithms to watch this seriously awesome video.   In terms of Microsoft’s research I want to focus on the relatively small size of their test corpus, it’s overlap with the training data, and the fact that the chosen corpus appears cherry-picked.

Corpus Size

The test set Microsoft selected for calculating the reported  5.1% WER is the 2000 NIST CTS SWITCHBOARD corpus.  While I was unable to find the specific number of hours of conversation in this test corpus I was able to confirm that the 1998 and 2001 NIST CTS data sets contained 3 and 5 hours of conversation respectively.  We can therefore assume the number of hours of conversation in the 2000 set is similar in duration.   When considering the overall size of the conversational speech domain explained in my previous post  a test set of this size is hardly sufficient for making any broad claims about meeting or beating human transcription accuracy.

Training Data Overlap

As you dig into the details of the NIST corpus a dirty little secret is quickly revealed.  Let me start by quoting directly from the source:

“Of the forty speakers in these conversations thirty-six appear in conversations of the published Switchboard Corpus.”

Let me translate that for you.   Thirty-six of the speakers in the test corpus are the same speakers used in Microsoft’s training corpus.   I’ll also remind you that the Switchboard corpus only has 543 speakers to begin with.  This raises a foundational questions about whether the test data is really distinct relative to the training set.   You see almost all modern speech recognition systems use something called i-vectors to help achieve speaker independence (sometimes called speaker adaptation).  Since the same speakers, on the same devices, in the same environments exist in both the training and test corpus there will invariably be a correlation between the i-vectors generated by the two data sets.

Per the diagram below, a truly honest measure of WER would require the the test data be truly distinct from the training set .  In other words it should pull from a data set that includes different speakers, different content, and different acoustic environments.   What is clear from the Microsoft paper is that this didn’t happen which calls into question whether the published results will truly generalize.  It also greatly diminishes the the validity of any claim about a new “milestone” being achieved in conversational speech recognition.

Cherry-picking

It’s worth noting that the full 2000 NIST CTS corpus actually contains a total of 40 conversations.   Twenty of those conversations are from the Switchboard corpus and twenty are from a different corpus called “Call Home”.   This begs the question of why Microsoft only validated against the Switchboard portion of the corpus.   While I can’t say for sure what their intent was, my best guess is because if they had used the Call Home data the results would not have led to the desired goal of meeting or beating “human accuracy”.

Taken altogether, the small corpus, with overlapping data, and a cherry picked data set you can’t help but ask did Microsoft really achieve a “new conversational speech recognition milestone”?

Is it Production Ready

EBTKS.  For those not familiar with texting slang, that stands for “Everything But the Kitchen Sink”,  and it’s really the best description of the system Microsoft used for this research.  This calls into question the production viability of their proposed solution.

Ensemble Models

At the acoustic model (AM) and language model (LM) layer Microsoft is using an ensemble model technique.   This technique requires training multiple models and processing each utterance through every model.   A separate algorithm is used to combine the outputs of the different models.   In essence this equates to trying to run multiple recognizers at once for every audio utterance.  It currently requires an enormous number of machines to transcribe phone calls in real-time at scale   Microsoft appears to be running 4 distinct AMs and multiple LMs which will have serious performance impacts.   This raises questions about the number of machines and associated costs required to run a system like the one used in Microsoft’s paper.

Language MODEL RESCORING

On top of the ensemble modeling Microsoft is also using language Model Rescoring.  In order to rescore you usually have an initial language model produce an N-BEST lattice which is basically the top N paths predicted by the language model.   This lattice needs to be stored or held in memory in order for the rescoring to take place.   In Microsoft’s case they are generating a 500-best lattice.   While not crazy holding a 500-best lattice in memory in a scaled production speech recognition system would not be ideal unless it provided significant accuracy gains.   According to the paper the gains from rescoring were minimal at best.

In Conclusion

So where does that leave us?  Microsoft has done some great research on advancing speech recognition algorithms.   Research that I greatly appreciate and hope to review further.   However for Microsoft to even imply that they achieved some epic milestone in matching human transcription accuracy is downright preposterous.

In the words of renowned Johns Hopkins speech recognition researcher Daniel Povey:

“… … this whole competition between IBM and Microsoft on Switchboard is just a pissing contest, in which they both try to add in more data and bigger system combinations to beat the other one’s number.  It doesn’t really indicate any special progress.”

I probably sang that Manfred Mann song a thousand times in my teen years and I was pretty sure the last word in that lyric was a feminine hygiene product until Google came along and taught me otherwise.   It turns out the lyrics to Blinded by the Light are very difficult to understand and so is conversational speech.

For my first substantive blog post on this site I’d like to continue on a theme we have been covering over at Marchex around the complexity in building automatic speech recognition (ASR) systems that can accurately understand unbounded conversational speech.   In this post I intend to dive a little deeper into WHY conversational ASR systems are so difficult to build, possible solutions to improve them, and the bounty for those who finally succeed.

There are really three primary issues that are limiting current systems from accurately recognizing conversational speech: Data, Data, and Data.    More specifically: Required Data Size,  Lack of Publically Available Data Sets, and Cost and Complexity with Acquiring the Required Data.

Required Data Size

There is no strict answer for how much data is needed to solve a given machine learning problem, but one oft-cited rule is the “rule of 10”.   The rule of 10 states that you need roughly 10 times as many examples as you have parameters.   While there are multiple parts of an ASR system including an acoustic model (AM) and a language model (LM), for now I am going to focus on the LM.   One parameter used in an LM is called an n-gram, specifically in most cases a trigram.   A trigram is basically the probabilities of any 3 words being seen next to each other.   So if we take the rule of 10 that would imply we need 10 times the number of 3 word combinations required for our task.

This is where the problem arises.  You see we humans write beautifully but we speak like idiots.   Grammar goes out the window when people talk, we stutter, words are often repeated over and over while people search for their next thought, and honestly some folks downright make up words that don’t even exist.   Taken together that means one can expect to see almost ANY combination of 3 words in the wild.   Everything from “a a a “ to “zebra zebra zebra” .   So if you don’t mind rewinding your brain to highschool math and combinatorics that means the number of 3 word combinations is:

| Number of Words in US English | ^3

or

| ~500,000 | ^3 = 125 QUADRILLION (i.e. a really #$%&’ing big number)

If we apply the rule of 10 we would need 1.25 QUINTILLION (i.e. an even bigger #$%&’ing number) utterances (basically a spoken sentence) containing examples of these trigrams.   Let me put this in perspective for you.    A single spoken utterance saved in a text file is roughly 50 bytes in size.   So in order to to store 1.25 QUINTILLION utterances I would need 50 * 1.25 QUINTILLION bytes of storage.  Or … 62,500 Petabytes!   For reference 20 years of internet archiving only consumed 23 petabytes as of 2015.  And if that doesn’t frame it for you think about it this way.   The average utterance duration is roughly 1.5 seconds. If I were to string 1.25 QUINTILLION recorded utterances together it would take approximately 60 millennia to play it back!

So what’s the point?   The point is that the data size required to cover all possible examples of spoken US English is almost inconceivable.  Is the rule of 10 an exact science?  No.  Does it matter?  No, because even if this estimate is wrong by 1/2 or 3/4 it is still huge.   Ultimately the data size needed to properly train a conversational ASR system is gargantuan.

Lack of Publically Available Data Sets

Okay so we need a lot of data.   Can’t we just buy it?  No!  Most publically available data sets are shockingly small compared to the size of the domain I described above.  As my fellow Marchex coworkers reported in our recently published research paper the size of 2 of the most commonly used data sets, fisher English and switchboard, is prohibitively small.

	Switchboard	Fisher English	Marchex
Hours	309	2,000	5,000+
Speakers	543	20,407	605,000
Utterances	391,000	1,600,000	11,700,000
Conversations	2,400	16,000+	288,000
Words	3,000,000	18,000,000	79,500,000

Dat Acquisition Cost and Complexity

Alright if you can’t buy it why don’t companies just go and collect the data themselves?   Well it turns out collecting 62,500 petabytes or 60 millennia’s worth of people conversing is no simple task.   There are two primary problems, collecting that amount of audio data and labeling it.

Audio Data

Where could someone acquire that quantity of audio data?   Well, there are countless hours of TV and Radio interviews out there but the dialog is generally scripted and edited so not reflective of true conversational speech.  On top of that in most cases companies do not have the legal rights to the data and acquiring those rights would be prohibitively expensive.

Amazon, Apple, Microsoft, Google, and other companies are all collecting mountains of data from various voice assistants (Alexa, Siri, etc.) and voicemail messages.   However all that speech data is mostly unidirectional and non-conversational (“Alexa tell me the weather” is not really conversational).

That leaves one obvious channel for acquiring conversational speech and that is phone calls.    So why don’t companies just collect call recordings at scale? The answer is simple:  WIRETAPPING.

In the US wiretapping is a federal and state statute aimed at ensuring your communications are private and there are criminal and civil penalties for those who violate the law.   What makes wiretapping laws particularly problematic is that the law varies by state specifically around who must consent to being recorded.

So why does this matter?   Well because 12 states require bidirectional consent and phone networks are open (nobody can guarantee they control both sides of the call).  While any company can update their “terms of service” to notify you that you are being recorded, they would have no easy way to guarantee that the other party has consented.   Unless they start playing that pesky message “this call might be recorded or monitored” in front of every call, including your weekly call with your mother!  This puts scalable call recording for consumer oriented phone services mostly out of reach since the risk of violating a criminal law is too high (I think it is safe to say Mark Zuckerberg has no interest in going to jail).

In fact just ask Google who has dealt with an ongoing wiretapping case because they were scanning the emails of GMAIL users to place targeted ads.   The argument is in fact incredibly similar in that Google was not just reading the email of GMAIL users but also any yahoo, Hotmail, etc. user who sent a GMAIL user an email.   In the 12 states requiring bidirectional consent the non-Gmail users never consented which has potentially caused Google to violate the law.

Labeling

Even if by some miracle we could collect that amount of audio data how would we label it?   In general ASR systems (and all other machine learning systems) require accurately labeled data (sometimes called “ground truth”).   In the speech recognition world that generally involves hand transcribing data.   And if it would take 60 millennia to read out that much speech imagine how long it would take to hand transcribe it.   Simply put, it is not feasible in our lifetimes at any reasonable cost.

What’s the Solution

It turns out almost all companies record phone calls.  Recordings from any one company would have highly biased content but in aggregate consumer to business recorded phone calls are an amazing source of conversational speech at scale.   Because you need a  wide cross section of content to ensure subject matter diversity, companies who provide platform call recording solutions and have legal access to the aggregate data are really the best sources of this content.

But what about the labeling?  Well the only reasonable solution for labeling that content is using unsupervised or semi-supervised automated solutions for labeling the data.   This is where Marchex has invested and you can read more details about our semi-supervised approach in our research paper.   I hope to cover this topic in detail in a future post.

Why Does Any of this Matter

You might be asking if highly accurate conversational speech recognition is really necessary.   Or you might be thinking “My Alexa already works awesome”   But if you are a sci-fi nerd like me you’re anxiously awaiting the day that you can step foot on the holodeck and have a real conversation with an AI entity or crack open a beer with a fully conversant robot like Data from Star Trek.   For that to happen we need to truly understand conversational speech.  We need to understand it so machines can properly decipher what humans are saying and we need to understand it so machines can generate speech that mimics human dialog.

Highly accurate conversational speech recognition is necessary for us to fulfill the promised vision of artificial intelligence.  Who knows maybe in a few years a holographic Manfred Mann and I will be doing a duet in my own personal holodeck.   Can you hear it?   “Blinded by the light, revved up like a deuce …”