Reading Oliver Burkeman’s last advice column in decade-long series in The Guardian, I was struck by his advice on the imposter syndrome:

The solution to imposter syndrome is to see that you are one…Humanity is divided into two: on the one hand, those who are improvising their way through life, patching solutions together and putting out fires as they go, but deluding themselves otherwise; and on the other, those doing exactly the same, except that they know it. It’s infinitely better to be the latter (although too much “assertiveness training” consists of techniques for turning yourself into the former).

After installing Stanza as dependency of UDAR which I recently described, I decided to play around with what is can do.

Installation

The installation is straightforward and is documented on the Stanza getting started page.

First,

sudo pip3 install stanza

Then install a model. For this example, I installed the Russian model:

#!/usr/local/bin/python3
import stanza
stanza.download('ru')

Usage

Part-of-speech (POS) and morphological analysis

Here’s a quick example of POS analysis for Russian. I used PrettyTable to clean up the presentation, but it’s not strictly-speaking necessary.

One of the challenges that Russian learners face is the placement of syllabic stress, an essential determinate of pronunciation. Although most pedagogical texts for students have marks indicating stress, practically no tests intended for native speakers do. The placement of stress is inferred from memory and context.

I was delighted to discover Dr. Robert Reynolds’ work on natural language processing of Russian text to mark stress based on grammatical analysis of the text. What follows is a brief description of the installation and use of this work. The project page on Github has installation instructions; but I found a number of items that needed to be addressed that were not covered there. For example, this project (UDAR) depends on Stanza; which in turn requires a language-specific (Russian) model.

sed is such a useful pattern-matching and substitution tool for work on the command line. But there’s a little quirk on macOS that will trip you up. It tripped me up. On most platforms, \s is the character class for whitespace. It’s ubiquitous in regexes. But on macOS, it doesn’t work. In fact, it silently fails.

Consider this bash one-liner which looks like it should work but doesn’t:

# should print I am corrupt (W.Barr)
# instead it prints I am corrupt by W.Barr
echo "I am corrupt by W.Barr" | sed -E 's|^(.+)\sby\s(.+)|\1 (\2)|g'

What does work is the character class [:space:]:

Since I’m not fond of carrying around all my photos on a cell phone where they’re perpetually at list of loss, I peridiocally dump the image and video files to a drive on my desktop for later burning to optical disc.¹ Saving these images in archival form is a hedge against the bet that my existing backup system won’t fail someday.

I’m using Blue-Ray optical discs to archive these image and video files; and each stores 25 GB of data. So my plan was to split the iPhone image dump into 24 GB partitions. H

In a previous post I described macros to support certain tasks in generating source material for L2 chorus repetition practice. Today, I’ll describe two other macros that automate this practice by slowing the playback speed of the repetition.

Background

I’ve described the rationale for chorus repetition practice in previous posts. The technique I describe here is to slow the sentence playback speed to give the learner time to build speed by practicing slower repetitions. By applying the Change Tempo... effect^[Change tempo effect in the Audacity manual] in Audacity. In my own practice, I will often begin complex Russian sentences at -50% speed and progress to -25% speed before practicing the pronunciation at native-level speed. By practicing at slow speeds, it gives the learner time to appreciate how syllables are connected to each other. The prosody is more apparent.

Achieving fluid, native-quality speech in a second language is difficult task for adult learners. For several years, I’ve used Dr. Olle Kjellin’s method of “chorus repetition” for my Russian language study. In this post, I’m presenting a method for scripting Audacity to facilitate the development of audio source material to support his methodology.

Background

For detailed background on the methodology, I refer you to Kjellin’s seminal paper “Quality Practise Pronunciation with Audacity - The Best Method!” on the subject of chorus repetition practice. The first half of the paper outlines the neurophysiologic rational for the method and the second half describes the practical use of the cross-platform tool Audacity to generate source material for this practice.

This is an update to my previous post on automating iTunes on macOS to support chorus repetition practice. You can read the original post for the theory behind the idea; but in short, one way of developing prosody and quality pronunciation in a foreign language is to do mass repetitions in chorus with a recording of a native speaker.

Because in macOS 10.15, iTunes is no more, I’ve updated the script to work with the new Music app. It turns out that it’s a lot simpler. No need to dive into the application classes.