So What? Marketing Analytics and Insights Live
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In this episode of So What? The Trust Insights weekly livestream, you’ll learn how to use generative AI for voice generation. You’ll discover various tools and techniques for creating realistic and engaging AI voices, along with ethical considerations and best practices. Explore practical applications for AI voice generation, such as podcasts, audiobooks, and accessibility, and enhance your content creation workflow with this innovative technology. Finally, you will also hear the hosts’ opinions and experiences about using generative AI for voice generation.
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In this episode you’ll learn:
- What is text-to-speech AI voice generation?
- The current best AI voice generation services to use
- How do you choose an AI voice generation service?
Transcript:
What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.
Katie Robbert: 00:00
So well, hey everyone. Happy Thursday! Welcome to So What? The Marketing Analytics and Insights live show. I’m Katie, joined by Chris and John. We’re all in one place this week.
Christopher Penn: 00:47
Hello.
John Wall: 00:51
Underwhelming five.
Katie Robbert: 00:53
Yeah, sorry. This week we’re talking about using generative AI for voice generation. Yes, we are going to show you how to create something that is adjacent to you. Well, it’s one of the goofy games that my husband and I like to play when we’re driving around or watching TV. Is it AI or not? Because a lot of the voices—a lot of the commercials—a lot of, if you watch a YouTube video, it’s like top eight places to get sushi—a lot of the voiceover narration is AI. Some of it is obvious AI, and some of it is not so obvious AI. So there’s definitely some really good tools out there.
Katie Robbert: 01:38
A lot of the TikTok and Instagram reel voices sound like there’s one that’s very clearly AI-generated, but everyone uses it. We’ve tried—mostly successfully—to create AI for voice generation for ourselves. We do have a Katie version. So far, the feedback has been, “Well, that’s not you.” Even though it’s my voice being used as the training data, the output is still not quite me. Chris, where would you like to start this week on generative AI for voice generation?
Christopher Penn: 02:29
I think the best place to start, of course, would be the 5P framework. Yay.
Katie Robbert: 02:37
Well, it makes sense.
Christopher Penn: 02:41
It does. And specifically voice generation—the technical term is called text to speech. So there are two variants of audio: TTS, or text to speech, which is turning text into speech; and ASR, automatic speech recognition, which is taking speech into text. Today, we’re going to cover text to speech. You give a machine text, and out comes sound. Let’s talk about some reasons why you might want to do this. One is accessibility. If you have content—say, on your website—and you have people who are vision-impaired, you can provide an audio version.
Christopher Penn: 03:29
One thing people forget is that it’s not just vision-impaired people who find value in audio content. Many people—call it what you want, neurodivergence, or whatever—want content in audio format so they can listen while doing something else. That’s why we have podcasts; people can listen while commuting, at the gym, or in the kitchen.
Christopher Penn: 04:07
I want to keep up on things, but I need to be able to listen only. Accessibility matters greatly. Another reason to use text to speech is that you just need content in different formats. Maybe you want a podcast, but you don’t have a good microphone setup, or maybe you just don’t want your voice on the internet. Turning long-form content into audiobooks is exhausting—16, 24, 30 hours in a booth, reading aloud and maintaining energy.
Christopher Penn: 04:57
We use GPUs—they all need cooling—and that kind of audiobook voice gets tiring. You might have dedicated audio guides. Imagine you’re in travel and tourism and want to generate an audio guide for a museum. Your museum changes exhibits often, and you don’t want to keep rehiring the same voice. Text to speech generates new audio guides.
Christopher Penn: 05:38
Or maybe you need audio guides in English and Spanish. When we survey our members, 15% speak Spanish. Do we have Spanish-speaking staff? No. Okay, text-to-speech, translating it, of course, voiceovers. I’m going to play a short voiceover. This is from a workshop we did last week: “Your next landmark office or mixed-use project demands more than just design. It demands certainty. At Southern Isles AEC, our integrated architecture and engineering teams eliminate surprises. Delivering complex, high-performance spaces on schedule, on budget, maximizing your assets, long-term value, experience, true partnership, and sustainable design that performs. Southern Isles AEC.”
Christopher Penn: 06:49
Building your vision reliably. Learn more at SouthernIslesAEC.com. That’s an example of a voiceover and an ad. You might have an IVR phone tree and want to upload new files. If you have a customer service line and own a restaurant, you might say, “Thanks for calling our restaurant. Today’s specials are…” You don’t want to record that every day. Finally, you want audio content online for AI to train on.
Katie Robbert: 07:30
I desperately wish this technology existed 15 years ago when I created a computerized version of a substance abuse intake tool. It had a voiceover component—a computerized assessment—and you could turn the audio off, but most people listened because it read the questions aloud. The population we served had lower literacy levels, so they relied on the voiceover. Because it was an intake tool for substance abuse, as new opiates and stimulants hit the market, we had to update the tool.
Katie Robbert: 08:25
Every time, we had to go back to the agency and hire an actor. If the actor’s pronunciation was off, we had to redo it. If the actor left, we had to find a replacement. The product ended up with four or five different voices, which was jarring. The human actors kept switching jobs.
Katie Robbert: 09:12
I wish this technology existed then! I would have taught myself to code to avoid dealing with casting actors and waiting a week, only to hear my manager ask, “Why can’t we do it faster?”
Christopher Penn: 09:34
Exactly. We said this at the workshop last week: text to speech is incredibly convenient and flexible. However, we should remember to hire human voice actors, too. We don’t want to eliminate them. The general consensus is this: if you’re creating audio content that’s a performance—like an audiobook—and you want to guarantee copyright, use a human. Presumably, you have the rights to the text itself.
Katie Robbert: 10:58
I’d add that for the Trust Insights brand, I don’t want some random AI-generated voice. I want one of us—people know and recognize our voices—so they can go, “Oh, that’s Katie’s voice, or John’s voice. It must be Trust Insights.” I don’t want a competitor to use the same AI voice and confuse people.
Katie Robbert: 11:43
That’s not to say someone couldn’t spoof our voices, but our voices are the voices of our company, our brands. I don’t want AI to stand in for me.
Christopher Penn: 11:56
That brings us to the tools for generation. The landscape looks like this: there are three variants. Pure text-to-speech tools are dedicated to making text to speech. Hybrid tools—like your operating system (Mac or Windows)—have text-to-speech components. You can highlight text and have it read aloud. There are tools like NotebookLM that aren’t designed as text-to-speech tools, but they have those capabilities.
Christopher Penn: 12:39
There are three variants: speech generators (like Google’s text-to-speech); cloners (like 11 Labs, which lets you clone your voice); and open-source tools, which are hybrids. Open-source tools have a high barrier to entry, but are inexpensive to use. The cost is compute. We need to consider five factors: cost, speed, volume, quality, and ethics.
Christopher Penn: 14:09
People wonder if you need real-time or canned products. If it’s a podcast, it doesn’t need to be real-time; but if it’s an interactive voice guide on your website, it does. Many web interfaces can’t handle more than a page of text—often, there are 5,000-character limits—so if you need big chunks, you’ll need to use a different approach.
Christopher Penn: 14:49
To convert an entire book to an audiobook, you’d buy a subscription to 11 Labs or upgrade to Google Cloud and feed your book in programmatically, piece by piece. It’s a pain.
Katie Robbert: 15:08
Quality.
Christopher Penn: 15:09
What quality do you want? It’s the old “fast, cheap, good” thing—choose two. What voice do you want? Brand alignment matters. Should it sound like Chris Penn or Katie Robbert? Or is it okay if it sounds like Google’s voice? Do you have the rights to use those voices? Just because you can doesn’t mean you should. I have some celebrities cloned in my 11 Labs account because I was able to before the restrictions. I don’t have the rights to use them.
Christopher Penn: 15:55
In any public-facing content, I ethically cannot use those voices. I can use them for my own amusement, but that’s about it.
Katie Robbert: 16:02
Mm, John, I don’t know if you’ve done this, but when I’m writing in a word processor like Google Docs or Microsoft Office, they have the ability to read back to you out loud. It falls under free bad quality, but if you’re editing, it’s helpful. Is that a use case you’ve ever used?
John Wall: 16:38
Yes, as Chris mentioned before: recording an audiobook. I’ve had the manuscript sent to two different editors and cleaned up. And still, I’m like, “Oh wait, that’s not right.” Until you have a human read it aloud, you’re still not done. The opportunities are amazing—automated things like weather reports or menus that change all the time. This makes that painful headache go away.
Christopher Penn: 17:18
Exactly.
Katie Robbert: 17:18
When I call my vet’s office, the chief surgeon records the menu. If they change the menu, he has to re-record it. His time is better spent treating animals than recording telephone menu options.
Christopher Penn: 17:48
One would think so. That brings us to the process. If you’re going to do voice cloning, 11 Labs has a decent setup. It’s garbage in, garbage out. If you record crap on your iPhone, it’s going to sound like crap. You need high-quality samples recorded with a good microphone. If you don’t have access to one, Adobe Podcast is an audio improvement tool.
Christopher Penn: 18:26
In Adobe Podcast, enhanced speech can clean up speech and make it sound more studio-like, but it can’t turn crap audio into great audio. If you have nothing but your phone, you can record, and then use Adobe Podcast to clean it up.
Katie Robbert: 19:17
That’s a really good option for a lot of people because not everyone has access to a good microphone or the space for one.
Christopher Penn: 19:29
Exactly. Not something you want to pack in your suitcase.
Katie Robbert: 19:35
I am that person.
Christopher Penn: 19:36
Text-to-speech machines don’t read like we do. You have to process text for speakability—taking out function formatting. I’ve simplified this with a project prompt in generative AI. We’ll put a copy in Analytics for Marketers.
Christopher Penn: 20:25
It basically says to fix things and produce two versions—one for a human narrator and one for an AI narrator. My writing isn’t easy to read aloud.
Katie Robbert: 20:45
I thank you for saying that. No, it is not.
Christopher Penn: 20:50
This example is from last week’s newsletter. It’s turned numbers into words and split things out. For fun, I’ve tried the original—it doesn’t sound good.
Katie Robbert: 21:27
If you’re not subscribed to our newsletter, you can get it at TrustInsights.ai/newsletter. We’re making it available in many different ways—written newsletter, YouTube, and podcast. I read the newsletter, but every week, I have to get through whatever Chris writes. I swear he makes it more difficult every week—it’s like tongue twisters.
Katie Robbert: 22:15
I worry about saying things like “Nvidia” correctly. It was me—the human.
Christopher Penn: 22:36
Yes, it was human. Once the text is processed for speakability, you can send it to the TCS model. I recommend testing small snippets. For example, let’s go to 11 Labs. I’ll stop sharing so I can share the actual tab (because that’s the only way to share audio in Streamyard). We’ll use KDE 4.0 and have it read aloud this sentence.
Katie Robbert: 23:34
That’s 71,400 megawatts. The average US home uses 20-30 kilowatts. AI is using the same amount of power as 2.86 million homes.
Christopher Penn: 23:54
It’s not bad. The speakability version is smoother.
Katie Robbert: 24:04
That’s 71,400 megawatts. The average US home uses 20-30 kilowatts. AI is using the same amount of power as 2.86 million homes.
Christopher Penn: 24:23
It handles it better, partly because the sentences are longer. Speakability is important before feeding text to the model.
Katie Robbert: 24:42
Should we use the human version for the third comparison?
Christopher Penn: 24:46
You can, if you want to.
Katie Robbert: 24:49
I’m glad you brought up that sentence. I stumbled over the second or third sentence: “This means that if AI chips in US data centers are running full tilt…” I struggled because to me it wasn’t correct grammar, but it could be. It’s interesting to hear the machine read it. It sounds more robotic, but it is interesting to hear it read versus how I tried to read it. It’s good to compare and see if I’m reading it correctly.
Katie Robbert: 25:34
Or, do I need to rewrite it? It’s a good way to QA. It’s similar to having content read aloud for grammar and editing. If you’re preparing for a podcast or audiobook, having 11 Labs read it will help you determine emphasis, pauses, and whether it sounds funny.
Christopher Penn: 26:05
That’s the 11 Labs version. 11 Labs has an API, but this window only allows 5,000 characters—about 700 words. For longer pieces, you’ll need to use the API. Google’s text to speech is entirely API-based. They have great-sounding voices.
Christopher Penn: 26:56
You heard the KD version in 11 Labs. Here’s the Google version.
Speaker 4: 27:02
In this week’s Data Diaries, let’s talk about sustainability. One question that keeps coming up is how much of a sustainability impact AI has. We don’t know how much energy massive data centers use, but we do know how many GPUs have been sold. Nvidia has about a 98% market share of GPUs in data centers.
Christopher Penn: 27:37
There’s a bug—”GPUs,” not “GPU’s.”
Katie Robbert: 27:42
It’s GPUs.
Christopher Penn: 27:43
Yep.
Katie Robbert: 27:44
It stumbled over the name of the event because of how it was written.
Christopher Penn: 27:52
Even with a speakability prompt, there will still be issues. If you think a paragraph will have problems, put it in first, generate, listen back, and clean it up. People use NotebookLM—you can load documents, trigger a two-host conversation, and give prompts.
Christopher Penn: 28:39
For example, “This is brought to you by Trust Insights AI, and the hosts will be…”
Katie Robbert: 28:55
Welcome to your deep dive. Ever feel like digging for buried treasure, like in a mountain of marketing advice?
Christopher Penn: 29:05
Oh yeah, I know.
Katie Robbert: 29:06
These Trust Insights newsletters feel like that.
Christopher Penn: 29:09
Yeah.
Katie Robbert: 29:09
But instead of gold doubloons, we’re after golden nuggets.
Christopher Penn: 29:13
Golden nuggets of marketing wisdom.
Katie Robbert: 29:15
Yes.
Christopher Penn: 29:16
Just like any good treasure hunt, you can’t just find the raw data.
Katie Robbert: 29:20
Right.
Christopher Penn: 29:21
You get the Google Studio voices. If you don’t like how they read audio, you can have a two-speaker setup.
Katie Robbert: 29:48
I find this really interesting. I see use cases for a lot of companies, especially large ones. But for a small company like ours, if it’s either Chris or Katie’s voice, I’d have trouble putting this into production and saying it’s a stand-in for Trust Insights.
Katie Robbert: 30:23
That’s where the 5Ps come in—does it need to be one of the humans on our team, or is a generic AI-generated voice okay? It needs to be accessible.
Christopher Penn: 31:03
You can do hybrids. For example, if you’re a solopreneur, you can use basic system audio recording tools to have an interactive conversation. I’m going to pull up Google AI Studio, and Chat GPT has this in advanced voice mode in their mobile app. I’m going to choose streaming mode here. I’m going to chat as Gemini Flash, and for this, let’s use Fenrir. I’m going to turn on Talk. I forgot to share the tab. Let’s go to tab stream, Google Studio, start new stream. I didn’t like Fenrir’s voice. I’m going to go with AO’s.
Speaker 4: 32:11
It’s going well, thanks for asking. What about you?
Christopher Penn: 32:14
I’m doing okay. I was thinking we could have a conversation about marketing analytics, specifically Google Analytics 4, and why the interface is such a hot mess. What’s your take on it?
Speaker 4: 32:26
Marketing analytics and Google Analytics 4. Got it. It definitely has its quirks. What specific aspects of the interface do you find most frustrating or confusing?
Christopher Penn: 32:35
The entire damn thing was designed by monkeys.
Speaker 4: 32:41
Okay, so it sounds like you find the entire interface to be poorly designed. What specific issues make you say that? And what alternative would you suggest?
Christopher Penn: 32:50
I would suggest using an alternative product like Matomo.
Speaker 4: 32:53
Okay, so you’re suggesting Matomo as a better alternative.
Christopher Penn: 32:56
This is completely unprompted. You can use knowledge blocks (which we cover in our Generative AI Use Cases course) as preparatory material for it to read before the conversation starts, and then have a virtual conversation. You can be the star of the show.
Katie Robbert: 33:36
Otherwise, it’s gonna sound like psych students fresh out of college trying to be a therapist.
Christopher Penn: 33:45
Exactly.
Katie Robbert: 33:45
This short 30-second interaction made me want to throw my computer.
Christopher Penn: 33:51
Yeah.
Katie Robbert: 33:53
John, I have a question for you. You host Marketing Over Coffee. I know from recording an episode with you that you record it in chunks—first act, ad for a sponsor, second act, etc. Would you ever create a John Wall voice? People still expect it to be you. Would you consider creating a John Wall AI-generated voice to read sponsorship ads?
John Wall: 34:44
Some sponsors want it dead perfect to the script. A lot of them want it tied to the content. Every week it gets tweaked. For a larger podcast, I could see this as a great way to do dynamic ads—one show serving up different ads depending on the listener.
John Wall: 35:23
You could target ads. An advertiser only pays for the demographic that will buy their product. This gives you a lot of ways to go. For us, a big part of the content is our take on the product. They’re not just looking to get the right people and throw them a standardized message—they’re looking for more of a take. It’s bad news for voiceover artists if they can do tons of different ads without having to sit through and record them all.
John Wall: 36:05
But I’d argue that’s work most people don’t want to do. Doing the 65th flavor of a Coca-Cola ad is a tough grind.
Katie Robbert: 36:17
When can we expect the totally AI-voice-generated Marketing Over Coffee episode?
John Wall: 36:26
Just run that live one as is. Just keep asking, “What do you think? What do you think?”
Christopher Penn: 36:32
You could train it on your previous 10 shows, train the voice and speaking style for each, and use Google’s TTS and 11 Labs to ping-pong back and forth, recording the segments. MP3 files are headerless—you can chop them into pieces and each piece will play as though it were a complete MP3.
Christopher Penn: 37:16
Way back in 2007-2008, when Overcast started, there was PodShow. Their big innovation was dynamically generated ad placement. The hosts had to hit certain timestamps. The system was designed so that an ad would drop in when someone requested the MP3 file. As advertisers changed, if you pulled the same episode two months apart, you’d get a different ad. You could do that.
Katie Robbert: 38:03
Listeners, we need the totally AI Chris, AI John episode of Marketing Over Coffee. But in a world where there’s an AI Chris and an AI John, how do you ensure the information is correct—that it’s not hallucinating?
Christopher Penn: 38:36
It’s the same as for other forms of hallucination prevention. The more data you give, the better it performs. If you’re talking about lead scoring, provide an up-to-date knowledge block about lead scoring. You can follow the Trust Insights RAPPEL framework (TrustInsights.ai/RAPPEL) and have that initial boot-up conversation.
Christopher Penn: 39:19
Let’s turn on advanced voice mode. How are we doing this morning? I’m going to play the role of a marketing automation expert skilled at B2B lead scoring. Concisely explain what modern lead scoring practices are in 2025. Exclude information from 2024 or earlier.
Christopher Penn: 40:12
After following the RAPPEL process, you could turn on your recorder and have an interactive conversation with a generative AI tool. This is useful if you’re a solo business person or podcaster. You’ll create interesting content. How do you bounce ideas off the machine and brainstorm new stuff? I don’t record them because it’d be noisy.
Christopher Penn: 40:57
I do this for hours in the car. The conversation is logged as text. You can summarize the conversation in outline format. You might have 10 ideas for your next content series or podcast. It summarizes and gives you finished work product.
Katie Robbert: 41:34
But that’s not voice generation; that’s you yelling at your phone.
Christopher Penn: 41:38
That is voice generation on its side, not your side.
Katie Robbert: 41:42
I think a lot of people are stuck feeling like they have to use generative AI to create a voice doppelganger. Do you need that? Is that something you need to spend time building right now? We played around with it, tried to create a Katie version, and realized it was more efficient for real Katie to do the reading.
Katie Robbert: 42:32
I tried to have it read something we created. It didn’t work that way. The amount of work you have to give these machines to get it exactly the way you want it is a lot. You have to decide if you need a human or a machine. That goes back to the use cases—performance, accessibility, menu changes every day, speed, etc.
Katie Robbert: 43:11
Is it because you’re trying to get something out the door fast? Are you a small shop or large shop? Does it have to go with your brand? It’s all software development, and software development takes time.
Christopher Penn: 43:27
Yep.
Katie Robbert: 43:27
You can play around with it, but to get it into production, you should probably have a good plan.
Christopher Penn: 43:34
There’s one more aspect: speech-to-speech. It’s slippery slope territory. But we can try it. In 11 Labs, there’s voice changing. I’m going to hit record. I’m going to read this text: “In this week’s Data Diaries, let’s talk about sustainability…” It’ll take my original human-led voice and try to apply Katie’s voice.
Katie Robbert: 44:26
In this week’s Data Diaries, let’s talk about sustainability…
Christopher Penn: 44:39
That’s read with my cadence and speech style, but tonally shifted to Katie’s voice.
Katie Robbert: 44:48
Yeah, that Katie needs to lay off the espresso.
John Wall: 44:51
Yeah, I over-caffeinated. That was my first thought, too.
Christopher Penn: 44:54
Yeah, exactly. That’s the way I speak. But it allows you to capture more human nuance. If there’s something where you can match the person’s cadence and speaking style, it can apply the tonality to make it sound more like that person. That’s clearly still Chris, where you get Katie’s hat.
John Wall: 45:20
Right.
Christopher Penn: 45:21
There’s no doubt about that. This is a slippery slope because, again, going back to ethics, do you have the rights to use that voice? You can clearly see how this could be very badly misused.
Katie Robbert: 45:35
Oh yeah, no, I would drive to your house and punch you in the face if you did that to me.
Christopher Penn: 45:42
If we think about basic use cases, suppose you want to go on vacation for two weeks. You could front-load three weeks of content, or just say, “Hey, that week, AI Katie will read aloud the newsletter.” Whichever sounds close to actual Katie, I might have to have decaf.
Katie Robbert: 46:12
It’s funny because we speak differently. Someone could easily go, “Oh, yeah, that’s not Katie. Katie doesn’t speak that fast.” Or, “Chris doesn’t speak that well.” I speak slower than you.
Christopher Penn: 46:41
Yes. In ASR tools like Fireflies, one thing it shows you is words per minute. You speak about 140, and I speak at 192.
Katie Robbert: 46:52
That doesn’t surprise me. AI tools aren’t necessarily going to be able to factor in that I’m a slow thinker and slow to make decisions. On the podcast, when you ask me a question, I often pause because I want to process the information and think about it before answering. That’s how I speak. You call up data faster than I do.
Katie Robbert: 47:36
I don’t see how that necessarily translates into this AI generation because that’s hard to mirror.
Christopher Penn: 47:50
One thing to think about is disclosure. You do want to disclose the use of AI because people will know. If you say up front, “Hey, Katie’s on vacation this week, so you’re getting the AI version,” that removes the doubt.
Katie Robbert: 48:40
I’m going to start slipping in weird things in my speech so you know it’s me and not AI.
John Wall: 49:07
I’m going to start using the Matthew McConaughey voice for everything, just like Instagram.
Katie Robbert: 49:14
What about you, Chris?
Christopher Penn: 49:17
I might record some AI-based conversations to see how they go, and if people find them valuable.
Katie Robbert: 49:45
I’m an N of 1, ignore me.
Christopher Penn: 49:49
Or we could just open up our mutual friend Chris Brogan’s phone number and let people call him randomly.
Katie Robbert: 50:00
Please don’t sign me up for that. No, John doesn’t want that, either.
Christopher Penn: 50:05
That was the old days. That’s it for this week’s episode. Thanks for tuning in. We’ll talk to you next week. Thanks for watching. Subscribe to our show wherever you’re watching it. For more resources, check out the Trust Insights podcast at TrustInsights.ai/podcast and a weekly email newsletter at TrustInsights.ai/newsletter. Got questions about what you saw? Join our free Analytics for Marketers Slack group at TrustInsights.ai/analytics-for-marketers. See you next time.
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Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.
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