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Re: AUDITORY Digest - 28 Sep 2006 to 29 Sep 2006 (#2006-215)

What was the original subject of this post?

Do you know of any way to filter email by subject when the usual subject line
contains only a digest number?

This is why it helps those of us who need to sort email at work,
to use the original subject line,
as long as it remains relevant,
instead of the digest number, which tells little.
Using digest as a subject kills my ability to sort and read by subject!


If you must quote me, please put your comments first.
I have already listened to mine.

I read email with speech.
So it is not possible to scroll past the quotes without listening to them again,
to quickly get to the new information.

Thanks much again as always.

>From owner-auditory@xxxxxxxxxxxxxxx  Sat Sep 30 05:02:11 2006
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Sender: AUDITORY  Research in Auditory Perception <AUDITORY@xxxxxxxxxxxxxxx>
From: Jont Allen <jontalle@xxxxxxxx>
Subject: Re: AUDITORY Digest - 28 Sep 2006 to 29 Sep 2006 (#2006-215)
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Dear Laszlo,

Christine is of course correct. I would like to post 3 of many refs. For 
more, look in my book "Articulation and Intelligibility" published by 
Morgan Claypool, 2005.

If you are finding differently for an ASR system, then that just shows
that the HMM "Gain" is turned up way too high. By that I mean, its 
ignoring the input to some extent, and looking for words that it can put 
together that make some sense, and thus have a combined low entropy 
(independent of other phones that it recognizes).

Let me give an example: What if the spoken utterance was:
"The make had a blue type"
and assume the recognizer got, at the phone level:
"the make had a blue type" (100% correct),
then the recognizer would report:
"the man had a blue tie"

Get it?
What if your relative calls you on the phone, and leaves a message,
that gets transcribed
"a large ant was fried"
and then you listen to the message, and it is really
"your great aunt just died"
That wouldnt be too good, would it.
Maybe not a very good example. They are better taken off of a real system.

All the example I have are my personal phone messages (text and wav 
files), and they have things in them I cant make public.
But they can be pretty funny sets of errors, I'll tell you!

Tell us what really happened, please. I dont care how off topic it is.
Its not off topic, IMO.

A comment: It is my opinion that ASR people will not report the phone 
scores because they dont want their funding sources to dry up. Typically 
these phone scores are quite low (compared to human scores, that is), 
being in the 50-75% range, with no noise. When the SNR gets "down" to 
+10, things are falling appart, and at 0 dB SNR, the scores (in one case 
I know) are below chance. Yes below chance!
  Human phone error rates start at somewhere between 1.5-2 % error in 
quiet. At +10 dB SNR (AI~0.5), the Miller Nicely phone error rate was 
about 10%. At 0 dB the AI is about 0.2 (Allen 2005, JASA, Fig. 6) which 
gives a phone error rate of about 30%. The 50% point is about -6dB SNR, 
and an AI of about 0.06.

We have unpublished results (in review) where we repeated some of this 
and found 2% error in quiet (consonants scored from CVs), 10% at -6 dB 
SNR, and 50% error at -18 dB SNR. However, we found that there are 3 
sets of consonants, with one group of 5 consonants, having very high 
error. These bias the average numbers way up. The rest of the sounds (11 
of them) are much better than what I quote above. One group has an error 
of 0.5% error in quiet (5 errors per 1000 presentations).

I have run on too long.

Please tell us more!

Jont Allen


author={Bronkhorst, A. W. and Bosman, A. J. and Smoorenburg, G. F.},
title={A model for context effects in speech recognition},
note_={} }

author={Boothroyd, A. and Nittrouer, S.},
title={Mathematical treatment of context effects in phoneme
and word recognition},
journal={J. Acoust. Soc. Am.},
pages={101-114} }

author={Boothroyd, A.},
title={Speech preception, sensorineural hearing loss, and hearing aids},
booktitle={Acoustical Factors affecting Hearing aid performance},
editor={Studebaker, G. A. and Hochberg, I.},
publisher={Allyn and Bacon},
note_={} }

AUDITORY automatic digest system wrote:

 Date:    Fri, 29 Sep 2006 12:37:34 +0200
 From:    Toth Laszlo <tothl@xxxxxxxxxxxxxxx>
 Subject: reference needed (ASR)
 Dear List,
 I know that speech recognition is a bit off-topic here, but I don't know
 of a more proper place to ask this. A reviewer wrote to a paper of
 mine that "the fact that better phone recognition does not necessarily
 mean better word recognition is already known, and people have been
 talking about it very frequently. This should be made clear and perperly
 referenced in the paper". Unfortunately, I'm personally sure that I've
 never seen this written down, because it would have saved me a lot of
 work -- but, unfortunately, I had to learned it from my own failures,
 so I'm sure I won't be able to recall any references for this. I'm also
 unable to figure out how to turn this thing into a reasonable Google
 search term (actually, I've just managed to find a reference for just the
 opposite - that "better phone recognition undoubtedly leads to better word
 recognition"). So, if anyone can tell me any paper stating or showing
 results that "better phone recognition does not necessarily mean better
 word recognition", I would be very grateful.
                Laszlo Toth
         Hungarian Academy of Sciences         *
   Research Group on Artificial Intelligence   *   "Failure only begins
      e-mail: tothl@xxxxxxxxxxxxxxx            *    when you stop trying"
      http://www.inf.u-szeged.hu/~tothl        *
 Date:    Fri, 29 Sep 2006 07:16:41 -0400
 From:    Christine Rankovic <rankovic@xxxxxxxxxxxxxxxx>
 Subject: Re: reference needed (ASR)
 The statement of the reviewer--that better phone recognition does not mean
 better word recognition--is wrong.  It is possible that the reviewer could
 support this statement with data from poorly conducted speech recognition
 tests like, for example, those conducted with an inadequate number of speech
 items, or when mean scores comprise scores of too few listeners.
 Christine Rankovic