Toward the end of 2021, I had the opportunity to work on a super fun personal project that I have been excited to talk about. I was able to closely examine the speech of Bill Hader over a period of years and directly compare that speech to one of his most famous recurring characters on Saturday Night Live, Stefon Meyers. For those of you that are not familiar with Stefon, check out this video of one of his early appearances on the SNL segment Weekend Update.

If you compare Stefon’s speech to a Bill Hader interview, you can hear there is quite a difference.

First let’s get a little background on Bill and Stefon. Bill Hader is a comedic actor and voice actor originally from Tulsa, Oklahoma. Bill got his first big break in show business when he was hired on as an actor for Saturday Night Live in 2005. While working at SNL, he also worked on many movies such as Cloudy with a Chance of Meatballs, Superbad and Forgetting Sarah Marshall.

During his time at SNL he also worked to develop the character of Stefon with John Mulaney (a former SNL writer, now stand-up comic). The character of Stefon was written after John Mulaney received an email from a club promoter who was trying to entice him into coming out to a hot new night club. The email had ridiculous selling features for the club such as “a room with broken glass”. The character of Stefon was meant to be a heightened version of this that served as a correspondent for Weekend Update who would let the host (Seth Meyers) know about all of the “hottest clubs” in New York City that tourists need to check out.

The voice for Stefon was inspired by a barista at a local coffee shop that Bill would frequently visit. The voice given to Stefon is higher in pitch and is breathier compared to Bill’s speaking voice. The voice also has a very prominent and stereotypical gay lisp to it which is most noticeable on his repeated affirmations “yes yes yes yes yes yes yessssssss”.

All of this is good and fun, but why is this worth looking at and talking about? Well, Bill was on SNL from 2005-2014, and during that time he performed the character of Stefon a total of 14 times (from 2010 to 2014) and an additional two times after leaving the show. This means that, thanks to the power of the internet, we have access to longitudinal data for both Bill (who did several late night interviews during this time) and Stefon. This allows us to compare three things:

1. We can see how consistent Bill was in his performance of Stefon over a period of years

2. We can see whether Bill’s own speaking voice changes over the years

3. If Bill’s voice does change, do those changes affect Stefon, or does he just keep Stefon in this somewhat frozen state?

So, what factors can we look at to compare these two voices? The first factor we can talk about is their pitch. If you recall a few weeks ago, I wrote a quick summary on how we can determine a speakers pitch and the factors that can influence it. The biggest thing to keep in mind here is that the pitch of both Bill and Stefon are coming from the same vocal tract. There is no obvious physical difference we can point to between the two voices like height or gender so any changes in pitch are the result of intentional effort by Bill.

As I already pointed out, you can tell that the pitch of Stefon’s voice is higher than Bill’s own voice simply by listening to his earlier performances. Bill has an average pitch of about 111 Hz while Stefon’s end up being around 133 Hz. But a key observation here is that because we have access to years of recordings, we can look at his performances year by year and spot a trend that might go unnoticed when looking at all the data at once.

In this later performance from 2018 (after Bill had not performed the character for 4 years), the pitch of Stefon actually ends up being lower than Bill’s speaking voice! (122 Hz for Stefon compared to 139 Hz for Bill). What is going on to cause this change? Well, I can’t say for certain, but my theory is that the long break between performances likely played a role in this. So, the interesting pattern over time here is that while Bill’s pitch is slightly increasing over time, Stefon’s pitch is going in a downward trend and there is this definitive crossover in the later years of performances.

What else can we talk about in these performances? I mentioned earlier that there is a prominent lisp that Bill gives to Stefon that really draws out his “s” sounds, but how can we quantify this difference. We can do this using simple airflow physics and the concept of centre of gravity. The physics is not super complicated, I promise. What we are measuring here is the energy created by the air flowing through the small space you make in your mouth you make when producing an “s” sound.

You can hear what I mean by doing this quick experiment yourself: Try to say the word “sa” out loud. Now say it again, but this time try to make a big smile and put your tongue as close to the roof of your mouth as you can while still being able to say the word. When you said it the second time, it probably sounded a bit louder on the “s” sounds. If you had recorded these productions and looked at the spectrograms for them, you would be able to see that the second version had higher frequency sounds on it compared to the first one. Something like this:

Comparing the production of the word on the left (save) to the one on the right (shave), you can see how the one on the right is much darker at the beginning. This is because the “esh” sound has a lower centre of gravity and less energy is produced overall in that sound compared to an “s” sound.

We can quantify this difference by looking at the centre of gravity of each of these sounds, and from that we can infer differences in how they are articulated. Let’s go ahead and look at how the “s” sounds differ for Bill and Stefon.

Unsurprisingly, the centre of gravity for Stefon (7877 Hz) is a lot higher on the “s” sounds compared to Bill (6051 Hz), but again looking at the change over the years reveals a surprising trend. In 2018, Bill had a centre of gravity of 8244 Hz and Stefon had a centre of gravity of 8373 Hz. This year is much closer than any other year where recordings exist for both Bill and Stefon. Looking at each speaker over time, we see that Bill’s centre of gravity steadily increases over the years while Stefon remains relatively the same.

Remember for these sounds too, this is the same “s” sound produced both in character, and out of character. I had to use two completely different sounds to show you a similar scale of difference, but Bill Hader was able to do this using the same speech sound.

The fact that he was able to remain so consistent in how he was articulating sounds as Stefon while his own voice has so much variation in it is quite amazing. This is a true testament to Bill’s talent. Even more so when you remember that he had a long period between 2014 and 2018 where he did not perform as Stefon at all. For him to be able to come back after that long break when his own voice so obviously changed is very cool (to me at least).

So, this is basically just a lot of charts and numbers at the end of the day, but I hope that it was at least cool to see some of the practical applications of acoustic phonetics. While this is a fun and silly example, there are practical applications for these techniques. A great example of that could be legal cases where investigators are trying to match an unknown speaker recording to a suspect. I had a lot of fun doing the research for this and doing the complete writeup for this for course credit and these are the two most interesting findings that came out of this. I likely won’t return to the rest of the findings because it gets a little more technical, but if there is interest in it, I am always happy to reconsider that.

Thank you for reading folks! I hope this was informative and interesting to you. Be sure to come back next week for more interesting linguistic insights. If you have any topics that you want to know more about, please reach out and I will do my best to write about them. In the meantime, remember to speak up and give linguists more data.