Cloning my father’s voice from 2000 and the transformative promise of neural networks on Parkinson’s Disease research.
In May 2000, my family moved from Omaha, NE to a small town in Southwest Missouri called Nixa. I used to be seven and my brother was two.
Six years later, my father was diagnosed with Parkinson’s Disease (PD) after initially going to see the doctor a few slight tremor he had on one side.
He went two days without telling anyone. Even Mom.
Let me start by saying my father is alive. I’m lucky to still have each of my parents in my life. I just saw them last weekend for Mother’s Day and my brother’s birthday. I’m so thankful for that point.
But he has modified. His voice has modified as happens with many Parkinson’s patients. Many living with PD exhibit a muted vocal expression, speaking in a monotonous tone and conveying limited emotional range. But it surely’s been a slow burn — thank goodness.
We moved for work, which was the very thing that drove my father to get off the bed every morning. And when this latest, young hotshot school Superintendent got here in from Omaha, the local news took note.
The primary time my brother saw this footage was in 2017 at my father’s retirement roast. I used to be sitting right next to him. The look on his face as he watched and listened has stuck with me over time.
He doesn’t remember my dad’s voice sounding like that.
He was excited and likewise, I feel, somewhat bit dissatisfied that he didn’t get more time with this more charismatic-seeming version of our father.
AI won’t ever replace that, in fact, but within the whirlwind of dystopic AI predictions, there does appear to be promise for good as well.
It’s shockingly easy to clone voices now with just just a few seconds of audio.
Once I heard about ElevenLabs’ VoiceLab, a voice cloning tool, this concept immediately got here to mind. I isolated my father’s voice from the interview after which simply uploaded that audio export (.mp3) to ElevenLabs and bam.
It was my father’s voice from 2000, saying anything I typed right into a box.
Obviously there are a ton of ethical questions here, which is why you must at all times make sure you’ve obtained explicit and informed permission before using someone’s voice. But I used to be in a position to create an audio book version of his children’s book, Golf Ball Envy, in his former voice. Sharing this with my family led to laughter and real interest.
I asked him to read a brief note to my brother, Jacob.
Here’s the 2000 version of him reading it.
It type of permanently seems like he’s giving an interview to a TV station to me, but given higher audio quality inputs, you possibly can make it sound an identical.
Like on this sample I created using audio from an iPhone video shot in an empty room. It’s my brother’s real voice as he records this for Mother’s Day.
Now here’s the AI version of him saying hi to my girlfriend, Julia.
It even permits you to hear what your voice would sound like in one other language, as if you spoke it fluently. Here’s Jacob in Japanese.
I’m not the primary person to take into consideration this. Intel has a latest project called I Will All the time Be Me which “banks” the voices of those affected by motor neurone diseases in order that they will communicate using their very own voice.
Neural networks, just like the voice cloning tool I used here, show real promise of predicting Parkinson’s Disease before symptoms show up.
I first saw this on Twitter:
I uploaded the complete white paper to ChatPDF.com, which uses the facility of GPT to permit users to ask PDF files questions on its content.
What’s difference between the neural network models and the normal machine learning models?
Neural network (NN) models differ from traditional machine learning (ML) models of their ability to learn complex non-linear relationships between input features and output variables. NN models are composed of multiple layers of interconnected nodes that may learn and extract features from raw data without the necessity for manual feature engineering. In contrast, traditional ML models typically require manual feature selection and engineering to extract relevant information from raw data. NN models also can handle high-dimensional data with many correlated features, which may be difficult for traditional ML models. Moreover, NN models can provide interpretable results by identifying significant biomarkers that contribute to disease prediction, which is significant for clinical applications. Overall, NN models offer a strong tool for analyzing large metabolomics data sets and developing accurate prediction models for disease diagnosis.
Does it mention business availability of testing?
No, the file doesn’t mention anything concerning the business availability of testing. It mainly focuses on the event of an interpretable neural network (NN) framework to accurately predict Parkinson’s disease (PD) from blood plasma metabolomics data using machine learning (ML) approaches. The study goals to offer opportunities for early diagnosis of disease by analyzing metabolite biomarkers and to develop accurate prediction models for disease diagnosis using large metabolomics data sets.
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