Patience may be a virtue, but it is increasingly harder to find among U. In our hyper-connected culture, where our expectation is to purchase products or services on-demand, businesses that fail to adapt are at risk.

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A way to plot my pace over the course of a talk. How do I practice speaking slowly? It can add extra words and still come under half my error budget.

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And I want to do this cheaply and quickly, without having to listen to the whole talk and painstakingly count the words. That gives me, for each word, which bucket it falls in. Some things might be faster, some might be slower. I took a talk draft, made several two-minute chunks, and spent some time each day practicing them. Since every element of the partition is is the same minute, this is equivalent to getting the corresponding bucket.

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After a while I could comfortably hit wpm. If I visually select a snippet, I can also see how long that particular snippet takes. This means I can use something off-the-shelf and not worry about its accuracy.

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I need to emphasize that this entire process was pointless. JSON parsing is easy and building out queries is straightforward. I kludged out the first J script in about ten minutes. It very roughly corresponds to the following Python version, which I wrote in about three minutes:.

JSON, by contrast, is deed around heterogenous arrays, leading to an impedance mismatch.

Words per minute

I need to take existing talks and count my average words per minute. I want real data of how fast I slur my talks. After that I need to filter out the punctuation items. It gave me useful information that materially improved my speaking skills. They both were around words total. Once I have that, I want to count how many words are in each bucket. J can only store homogenous arrays. My mean is wpm.

In fact, terseness is an advantage here. I practiced saying that with a stopwatch and quickly found out that saying that in 25 seconds is just awful. A box can wrap any value, turning it into a single atom. So the short term goal: transcribe the audio from a video of my talk. After that, I added this to my vim config:. I assumed that transcription software would just output the text without any timing info.

This then consumed 2 hours of my life. The MVP is getting the total words per minute wpm. J uses boxing to get around this. This would let me find my overall speaking pace. Generating the minute counts mc is surprisingly easy in J, once you know the trick. A way to remove sections that are not me talking. I took notes through the project so I could share how I did things and the issues I ran into.

For example:. Now, when I open a file of filetype.

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I eventually got this:. I can do that by dividing each time by 60 and rounding down.

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Compare writing. Alternatively, I can break the audio into minute-long segments and transcribe them individually. At this point PocketSphinx has been running for 15 minutes. How fast do I talk, anyway? It looks like the graph, but the y-axis is different.

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I can just extract the start times into an array and analyze that. Python is a workhorse. This means automatic transcription. I also want to see how much this varies over time. Every element of the array must have the same type and every subarray must be the same length.

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Dictonaries are represented as 2-column tables, where the first element is the key and the second is the value. This makes it extremely difficult to store strings, let alone nested data! I leave it running and while I try out AWS. Total time, from discovering the service to getting the final file, is about 20 minutes. The other option is J. The big problem is going to be parsing the JSON. I spent even more time trying to optimize the runtime. To do that, I want to divide the times into bucket intervals, say 1 minute per bucket.

How fast does the average person speak?

If I want to get how fast I spoke during the talk itself, I need to filter out everything past the end of the talk, which was at seconds. Once the video went online, I transcribed it and did the same analysis: 4. What surprised me, though, was that the two talks covered the same amount of content. I want to slow down. Hm, maybe trying to transcribe 30 minutes of speech at once was a bad idea. I can do it if I concentrate but I quickly relapse into gushing out words. Then I could measure standard deviation, or how much my speaking rate varies over the course of the talk.

Average speaking rate and words per minute

Through my whole life people have told me to slow down, speak more clearly, and enunciate. That talk felt easier on me, but I wanted to confirm I really was speaking more slowly. Is wpm too slow? After I finished speaking there was five minutes of questions, which were less dense word-wise. The length of the array is the of words I said, so divide the length by the last time will give my words per second. I wanted to prove to myself that the J would be a lot terser if I could just reduce my character count more.

I found this guide to transcribing audio. While searching for that I find that AWS just released a new transcription service. Out of curiosity, I look up how much it would cost to transcribe it manually. It also runs much faster, completing in less than a tenth of the time. The talk is over 6, words long. I expect J to be both faster and more concise than Python, but neither is true here. How slow is slow enough?

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There will be a few errors in the array, because the transcription gets some of the words wrong. So about words a minute. This would be things like speaker introductions, applause at the end, questions. I want to hit wpm on averagenot for every single thing I say. This means the following two are the same type:. Including them will give me poorer data.

What is the average speaking rate?

I speak very fast. The start time is per word, in seconds. All elements of an array must be the same type, and all subarrays must have the same length. Finally, we stitch the two arrays together with. My latter talk was about 6 minutes longer.

A speech has pauses, meaningful silence, quotes, points you rush for emphasis, etc. The Python script is only slightly longer than the J script. Which is more elegant, trust me on this.

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