<div dir="ltr">Hello, <div><br></div><div>We looked into MLT and what it provided was the number of continuous utterances by a single speaker, but not whether it was contingent on a particular speaker. Apologies, my example did not include the case for multiple speakers as in the following so we would want: </div><div><br></div><div>MLT would provide: </div><div>3 turns for MOT</div><div>1 turn for GMA</div><div>3 turns for CHI</div><div><br></div><div>Whereas what we are interested in is 5 turns if we count each response as an initiation (specifying the child and MOT/GMA as adults):</div><div>MOT-CHI</div><div>CHI-GMA</div><div>MOT-CHI</div><div>CHI-MOT</div><div>MOT-CHI</div><div><br></div><div>Or 3 turns between an adult and child if we always take the utterances before as an initiation and do not count each response as an initiation (this is similar to how LENA calculates their automated conversational turn measure):<br></div><div>MOT-CHI</div><div>MOT-CHI</div><div>MOT-CHI</div><div><br></div><div><br></div><div><div style="color:rgb(0,0,0);font-family:Calibri,Helvetica,sans-serif;font-size:16px">*MOT:<span style="white-space:pre-wrap"> </span>come here !</div><div style="color:rgb(0,0,0);font-family:Calibri,Helvetica,sans-serif;font-size:16px">*CHI:<span style="white-space:pre-wrap"> </span>xxx .</div><div style="color:rgb(0,0,0);font-family:Calibri,Helvetica,sans-serif;font-size:16px">*GMA: come here and sit with me. </div><div style="color:rgb(0,0,0);font-family:Calibri,Helvetica,sans-serif;font-size:16px">*GMA: let's get you up here.</div><div style="color:rgb(0,0,0);font-family:Calibri,Helvetica,sans-serif;font-size:16px"><span style="font-size:12pt">*MOT:<span style="white-space:pre-wrap"> </span></span><span style="font-size:12pt">come here let's sing the A_B_C_s@s .</span><br></div><div style="color:rgb(0,0,0);font-family:Calibri,Helvetica,sans-serif;font-size:16px">*MOT:<span style="white-space:pre-wrap"> </span>ready ?</div><div style="color:rgb(0,0,0);font-family:Calibri,Helvetica,sans-serif;font-size:16px">*MOT:<span style="white-space:pre-wrap"> </span>a@l b@l c@l d@l . </div><div style="color:rgb(0,0,0);font-family:Calibri,Helvetica,sans-serif;font-size:16px"></div><div style="color:rgb(0,0,0);font-family:Calibri,Helvetica,sans-serif;font-size:16px">*MOT:<span style="white-space:pre-wrap"> </span>you don't want to sing ?</div><div style="color:rgb(0,0,0);font-family:Calibri,Helvetica,sans-serif;font-size:16px">*CHI:<span style="white-space:pre-wrap"> </span>no .</div><div style="color:rgb(0,0,0);font-family:Calibri,Helvetica,sans-serif;font-size:16px">*MOT:<span style="white-space:pre-wrap"> </span>are you sure ?</div><div style="color:rgb(0,0,0);font-family:Calibri,Helvetica,sans-serif;font-size:16px">*CHI:<span style="white-space:pre-wrap"> </span>no .</div></div><div style="color:rgb(0,0,0);font-family:Calibri,Helvetica,sans-serif;font-size:16px"><br></div><div style="color:rgb(0,0,0);font-family:Calibri,Helvetica,sans-serif;font-size:16px"><span style="color:rgb(34,34,34);font-family:Arial,Helvetica,sans-serif;font-size:small">Basically, we are interested in trying to get an automated conversational turn measure, that is similar to LENA's, but using transcriptions that we have already vetted as being child-directed speech with our adult speakers of interested already identified. </span><br></div><div style="color:rgb(0,0,0);font-family:Calibri,Helvetica,sans-serif;font-size:16px"><span style="color:rgb(34,34,34);font-family:Arial,Helvetica,sans-serif;font-size:small"><br></span></div><div style="color:rgb(0,0,0);font-family:Calibri,Helvetica,sans-serif;font-size:16px"><span style="color:rgb(34,34,34);font-family:Arial,Helvetica,sans-serif;font-size:small">Janet</span></div></div><br><div class="gmail_quote"><div dir="ltr">On Fri, Dec 21, 2018 at 8:40 AM Brian MacWhinney <<a href="mailto:macw@cmu.edu">macw@cmu.edu</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div style="overflow-wrap: break-word;">Nan,<div><br></div><div>Yes, you are right. MLT will do this. I tested on this small sample from Janet and it reports 3 turns for MOT and 3 for CHI.<br><div>
<br class="gmail-m_3825634855035477872Apple-interchange-newline"><span style="color:rgb(0,0,0);font-family:Helvetica;font-size:12px;font-style:normal;font-variant-caps:normal;font-weight:normal;letter-spacing:normal;text-align:start;text-indent:0px;text-transform:none;white-space:normal;word-spacing:0px;text-decoration:none;float:none;display:inline">-- Brian</span>
</div>
<div><br><blockquote type="cite"><div>On Dec 21, 2018, at 11:32 AM, Nan Bernstein Ratner <<a href="mailto:nratner@umd.edu" target="_blank">nratner@umd.edu</a>> wrote:</div><br class="gmail-m_3825634855035477872Apple-interchange-newline"><div><div dir="ltr">Wouldn't that be Mean Length of Turn? (MLT program?)<div>N</div><div><br clear="all"><div><div dir="ltr" class="gmail-m_3825634855035477872gmail_signature"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div>Nan Bernstein Ratner, F-, H-ASHA, F-AAAS, ABCLD</div><div>Professor</div><div>Hearing and Speech Sciences</div><div>University of Maryland</div><div>0100 Lefrak Hall</div><div>College Park, MD 20742</div><div><a href="mailto:nratner@umd.edu" target="_blank">nratner@umd.edu</a>, 301-405-4217</div><div><br></div><div>Co-director: FluencyBank (<font color="#0000ff"><a href="http://www.fluency.talkbank.org/" target="_blank">www.fluency.talkbank.org</a></font>); <a href="http://languagefluency.umd.edu/" target="_blank">http://languagefluency.umd.edu/</a> </div><div><br></div><div>President, International Fluency Association (IFA; <a href="http://theifa.org/" target="_blank">http://theifa.org</a>)</div><div>Director, University of Maryland Autism Research Consortium (UMARC), <a href="http://www.autism.umd.edu/" target="_blank">www.autism.umd.edu</a></div><div>Faculty, Language Science (<a href="http://languagescience.umd.edu/" target="_blank">languagescience.umd.edu</a>; Neuroscience & Cognitive Neuroscience (NACS, <a href="http://nacs.umd.edu/" target="_blank">nacs.umd.edu</a>), Developmental Science Field Committee</div><div><br></div><div><a href="http://hesp.umd.edu/facultyprofile/bernstein%20ratner/nan" target="_blank">http://hesp.umd.edu/facultyprofile/bernstein%20ratner/nan</a> </div><div><br></div><div><a href="http://www.ncbi.nlm.nih.gov/sites/myncbi/1-UVaaDdREZ/bibliography/40436718/public/?sort=date&direction=ascending" target="_blank">My PubMed Bibliography</a></div></div></div></div></div></div></div></div></div></div></div></div></div><br></div></div><br><div class="gmail_quote"><div dir="ltr">On Thu, Dec 20, 2018 at 8:34 PM Janet Bang <<a href="mailto:jbang@stanford.edu" target="_blank">jbang@stanford.edu</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">
<div dir="ltr">
<div id="gmail-m_3825634855035477872gmail-m_1985687063813677905divtagdefaultwrapper" style="font-size:12pt;font-family:Calibri,Helvetica,sans-serif" dir="ltr"><div style="margin-top:0px;margin-bottom:0px">Hello, </div><div style="margin-top:0px;margin-bottom:0px"><br>
</div><div style="margin-top:0px;margin-bottom:0px">We are trying to see if there's a way to automate a count of turns between a child and an adult within a transcript. We saw that with CHIP, we can get the number of responses to a child's utterance, but this value counts
multiple utterances as separate responses, rather than one turn. </div><div style="margin-top:0px;margin-bottom:0px"><br>
</div><div style="margin-top:0px;margin-bottom:0px">For example, for the following interaction we would count 5 turns: </div><div style="margin-top:0px;margin-bottom:0px">MOT-CHI</div><div style="margin-top:0px;margin-bottom:0px">CHI-MOT (MOT has 4 utterances, but count as 1 turn)</div><div style="margin-top:0px;margin-bottom:0px">MOT (same MOT as one above)-CHI</div><div style="margin-top:0px;margin-bottom:0px">CHI-MOT</div><div style="margin-top:0px;margin-bottom:0px">MOT-CHI</div><div style="margin-top:0px;margin-bottom:0px"><br>
</div><p style="margin-top:0px;margin-bottom:0px"></p>
<div>*MOT:<span style="white-space:pre-wrap"> </span>come here !</div>
<div>*CHI:<span style="white-space:pre-wrap"> </span>xxx .</div>
<div><span style="font-size:12pt">*MOT:</span><span style="font-size:12pt;white-space:pre-wrap">
</span><span style="font-size:12pt">come here let's sing the A_B_C_s@s .</span><br>
</div>
<div>*MOT:<span style="white-space:pre-wrap"> </span>ready ?</div>
<div>*MOT:<span style="white-space:pre-wrap"> </span>a@l b@l c@l d@l . </div>
<div></div>
<div>*MOT:<span style="white-space:pre-wrap"> </span>you don't want to sing ?</div>
<div>*CHI:<span style="white-space:pre-wrap"> </span>no .</div>
<div>*MOT:<span style="white-space:pre-wrap"> </span>are you sure ?</div>
<div>*CHI:<span style="white-space:pre-wrap"> </span>no .</div>
<br><div><br class="gmail-m_3825634855035477872webkit-block-placeholder"></div><div style="margin-top:0px;margin-bottom:0px"><br>
</div><div style="margin-top:0px;margin-bottom:0px">Is there some way to combine MLT and CHIP to get a value that represents turns as above? If that is too difficult, we could also go without the responses serving as the initiation of the next turn, meaning that the this
would be 3 turns as in:</div><div style="margin-top:0px;margin-bottom:0px">MOT-CHI</div><div style="margin-top:0px;margin-bottom:0px">MOT(1 turn, 4 utterances)-CHI</div><div style="margin-top:0px;margin-bottom:0px">MOT-CHI</div><div style="margin-top:0px;margin-bottom:0px"><br>
</div><div style="margin-top:0px;margin-bottom:0px">Apologies if I've missed this in another function, </div><div style="margin-top:0px;margin-bottom:0px">Janet</div><div style="margin-top:0px;margin-bottom:0px"><br>
</div>
<div id="gmail-m_3825634855035477872gmail-m_1985687063813677905Signature">
<div id="gmail-m_3825634855035477872gmail-m_1985687063813677905divtagdefaultwrapper" dir="ltr" style="font-size:12pt;font-family:Calibri,Helvetica,sans-serif"><div style="margin-top:0px;margin-bottom:0px">--</div><div style="margin-top:0px;margin-bottom:0px"><span style="font-size:10pt">Janet Y. Bang, Ph.D.</span></div>
<span style="font-size:10pt"></span><div style="margin-top:0px;margin-bottom:0px"><span style="font-size:10pt">Postdoctoral Fellow</span></div>
<span style="font-size:10pt"></span><div style="margin-top:0px;margin-bottom:0px"><span style="font-size:10pt">Department of </span><span style="font-size:10pt">Psychology</span></div>
<span style="font-size:10pt"></span><div style="margin-top:0px;margin-bottom:0px"><span style="font-size:10pt">Stanford University</span></div>
<span style="font-size:10pt"></span><div style="margin-top:0px;margin-bottom:0px"><br>
<span style="font-size:10pt"></span></div>
<span style="font-size:10pt"></span><div style="margin-top:0px;margin-bottom:0px"><span style="font-size:10pt"><a href="mailto:jbang@stanford.edu" target="_blank">jbang@stanford.edu</a></span></div><p style="margin-top:0px;margin-bottom:0px"></p><div style="margin-top:0px;margin-bottom:0px"><br>
</div><div style="margin-top:0px;margin-bottom:0px"><br>
</div><div style="margin-top:0px;margin-bottom:0px"><br>
</div><div style="margin-top:0px;margin-bottom:0px"><br>
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</div><div><br class="gmail-m_3825634855035477872webkit-block-placeholder"></div>
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