Yorick 2.0: The Personality Split


When Yorick was first brought to life, he had Alexa’s voice. A lot of his charm was the incongruity between his appearance and his voice. At the same time, a number of folks asked about having a creepier voice and I wanted to try to do that for this Halloween.  An update to the AlexaPi project added support for the SoX audio playback handler as an alternative to VLC. SoX has support for audio effects, so it became possible to change Yorick’s output voice. I didn’t want to lose Alexa’s voice, so I edited the AlexaPi code so that it would recognize both “Alexa” and “Yorick” as trigger words, with the output sound depending on which trigger word you used. As a result, Yorick now responds either as Alexa or with his own voice.

Just like Elliot on Mr. Robot, Yorick now has a split personality.


I talked to Yorick, aka Alexa, a bit about Halloween:

It turns out that Yorick is a baseball fan and was rather disappointed that the Washington Nationals aren’t in the World Series. Awhile back, I asked him about going to one of the playoff games:

Technical Notes

AlexaPi uses PocketSphinx for recognizing the trigger word. The original code is set up to recognize a single trigger word or phrase, which you can easilly change in a yaml configuration file. However PocketSphinx can recognize multiple keywords or phrases selected from a python list. Some editing of the AlexaPi source code was needed in order to change the trigger from a single variable to a list. Similarly, the code was modified slightly so that once a trigger word was recognized it checks which word was used. If the trigger word is “Yorick” it changes the pitch and speed of the audio output.

I used version 1.5 of AlexaPi. This and previous versions had a problem in that the temporary file names used were the response code that the Alexa voice service returned. These sometimes included characters that were illegal for file names or that were too long for a file name. I patched these problems (and later versions of AlexaPi have fixed this problem).

In addition, the servo motion routines had to be modified slightly, as version 1.5 and later of AlexaPi begins streaming questions to the Alexa voice service as they are asked, rather than waiting until the question is finished. This results in a faster response time.

A Bit More on Project Yorick

I’ve been extremely gratified by the interest in Project Yorick, so I thought I’d share a bit more. First up is a St. Patrick’s Day greeting:

And here’s a video of Yorick without the project box, as he was being developed:

Last Halloween, I accidentally applied too much voltage to the servos and burned out the eyes servo. Luckily, I could take the skull apart and replace it. Here’s a picture from the successful brain surgery:

Yorrick’s Brain Surgery


The Yorick Project

I like to decorate for Halloween, including various talking skeletons that I’ve set up over the years. For Christmas 2015, my wife gave me a great 3 axis talking skull with moving eyes so I could upgrade one of the skeletons from just a moving jaw skull. Then a friend suggested that there had to be other applications for the rest of the year. This got me thinking, and when I saw the Alexa Billy Bass I knew what I had to do, and the Yorick project was born. I’m pretty happy with the result:

Now, if you put this project together from scratch, it’s pretty expensive, due to the cost of the 3-axis talking skull, but if you are looking to re-purpose one you have, or a similar device, then you may want to develop a similar project. The key elements are the talking skull, a Raspberry Pi and the AlexaPi software for turning the Pi into an Alexa client device, the audio servo controller for turning the output sound into servo commands for the jaw, and the servo controller for controlling the nod, turn, tilt, and eye servos of the skull.

Block diagram for Yorick

Bench testing the set-up

The AlexaPi software provides output to two GPIO pins, intended to light up LEDs as the Pi hears the wakeup word, listens to the input, gets the response from the Amazon Alexa service, and then speaks the response. All the directions for AlexaPi are on the creator’s GitHub site. For this project, we also linked the same pins to input pins on the Maestro servocontroller. The Maestro I used allows pins to be used as output (primarily for servos, but also for other purposes) or as analog inputs. Other models also have digital input pins. By reading the status of the input pins, we know which state to be in, as there is a separate routine of head motions for inactive, wake, listen, get response, and speak response.

The servo sequences are developed using the GUI-based controller software provided by Pololu, and then custom control commands are added using their stack-based scripting language. The partial script is included at the end of the article. The short first section that is provided is the control code I wrote, the rest (not shown) are the automatically generated subroutines based on the sequences I defined using their GUI driven software.

The skull motions for each state are predefined and fixed (the routines are looped as needed for the typically longer lasting states (get response and speak response). The one key tip is to slow down the servos in order to look more realistic. The Maestro controller software lets you limit the speed and acceleration of each servo, and with the exception of the jaw servo, which had to have quick response to changing audio, I set both the speed and acceleration values to 20.

The audio servo driver board converts the audio put out by the Pi into servo commands for the jaw, while also passing the audio through to the powered speakers. Others have developed their own software do drive motor (rather than servo) based devices such as Billy Bass and Teddy Ruxpin, based on the amplitude of the sound. I’m sure the same could be done to drive the jaw servo by extracting the volume of the sound, but I already had an audio servo driver board that is otherwise unused except for Halloween, so I used that.

3-axis talking skull with moving eyes

Raspberry Pi with Kinobo USB microphone

Audio servo controller board

Mini Maestro Servo Controller board
















I put it all in a project box, and covered the threaded rod supporting the skull with a short length of PVC pipe, painted black, to produce the final result:

Rather messy (mainly with the 5 servo cables), but it all fits in the box.

Hardware Components

  • 3-axis talking skull with moving eyes. Mine is from audioservocontroller.com
  • Powered speakers – The Raspberry Pi puts out very little volume from the audio jack, so you want amplified speakers, whether battery powered or from line current. I used an old pair of inexpensive computer speakers
  • Raspberry Pi 3 – Other models with WiFi should also work
  • Kinobo – USB 2.0 Mini Microphone – You need a microphone for input, and I was quite happy with the performance of this one, considering it only cost $5.25!
  • Pololu Mini Maestro Servo Controller – I used the 12 channel version, larger versions should also work.
  • Audio servo driver board – I used an ST-200 board from Cowlacious that I had for a Halloween project. That model has been replaced by the newer and improved ST-400 model, which should work fine. audioservocontroller.com also sells a similar board
  • Misc., including project box, LEDs (optional), resistors (optional, if LEDs used), breadboard (optional, if LEDs used), PVC pipe, and jumper wires.

Software Components

  • AlexaPi open source client software for Amazon’s Alexa service
  • Pololu’s Maestro Controller Software – I used the Windows version, but they have versions for Linux as well.
  • Custom script for the Maestro servo controller to control the skull


begin     # Loop until input 9 goes high
  9 get_position # get the value of the red trigger, 0-255
  155 greater_than # test whether it is greater than 155 
  if      # Wakeup word detected, run wake movement sequence
    750 delay
    1 # flag for getting out of loop
      9 get_position
      10 get_position
      300 greater_than
      if     # If after wake, both inputs high, then getting reply
        9 get_position
        155 greater_than
        if     # if just input on 9 high, then speaking reply
          10 get_position
          155 greater_than
          if   # if just input on 10 high, then listening to query
          else     # when both inputs at zero, back to rest mode
            1 minus

### Sequence subroutines: ###
# Sequence subroutines removed not included for brevity