TechCrunch News 04月03日 23:13
Voice AI platform Phonic gets backing from Lux
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Phonic由麻省理工学院毕业生Moin Nadeem和Nikhil Murthy创立,致力于提供端到端的语音技术解决方案,以提高AI语音的可靠性并降低延迟。该公司通过自主训练模型,而非拼凑现有的AI模型,从而实现更深度的集成和更低的成本。Phonic的模型在各种录音(包括带口音和模糊的语音)上进行训练,以确保其鲁棒性。目前,Phonic正与保险和医疗保健领域的有限合作伙伴合作,计划在几个月内广泛推出其产品。该公司已在Lux Capital领投的种子轮融资中筹集了400万美元。

🗣️ Phonic由MIT毕业生Moin Nadeem和Nikhil Murthy创立,旨在构建端到端的语音技术解决方案,提高合成语音的可靠性并降低延迟。

💡 Phonic采用独特的方法,通过自主训练模型来实现深度集成,而非像Vapi和Rounded等公司那样将不同的AI模型拼凑在一起。这种方法使Phonic能够更有效地托管和运行模型。

🎤 Phonic的模型在各种录音上进行训练,包括带口音和模糊的语音,以增强模型的鲁棒性。该公司目前正与保险和医疗保健领域的有限合作伙伴合作,计划在几个月内广泛推出其产品。

💰 Phonic已在Lux Capital领投的种子轮融资中筹集了400万美元,投资方包括Replit、Hugging Face、Applied Intuition和Modal Labs的联合创始人。

The quality of AI-generated voices is good enough for things like creating audiobooks and podcasts, having articles read aloud to you, and basic customer support. But many businesses don’t think AI voice tech is quite reliable enough to deploy.

That’s why two MIT grads, Moin Nadeem and Nikhil Murthy, founded Phonic, a company offering an end-to-end voice stack to increase synthetic voice reliability while decreasing latency.

Nadeem and Murthy met at MIT, and have known each other for more than seven years. When the duo started building Phonic last year, they felt there weren’t many companies crafting complete voice tech solutions.

“Voice AI is at a place where you tie up different parts, such as automatic voice recognition [and] text-to-speech, and [then integrate] intelligence,” Murthy told TechCrunch. “However, when we talked to actual customers, we found that there is a lack of [solutions] that [are] reliable at scale.”

Nadeem, who previously worked at MosaicML, a company Databricks acquired for $1.3 billion in 2023, said that a lot of companies that are building in the voice AI space (e.g. Vapi, Rounded) are creating workflows to piece together separate AI models.

Phonic takes a different approach: it trains its models in-house end-to-end. Murthy said that there are a few advantages to this.

“Owning the models allows us to deeply integrate some […] reliability pieces into the [models themselves],” he said. “If you don’t own that layer […] you’re just tying disparate pieces that don’t really fit seamlessly.”

Murthy added that Phonic’s method also allows the company to host and run models cost-efficiently. He claims that Phonic trains its models on a range of recordings, including recordings of accented and muffled speech, to make the models highly robust.

Phonic is currently working with a limited set of partners, including companies in the insurance and healthcare spaces, but plans to launch its product broadly in a few months. Soon, prospective clients will be able to try out Phonic’s tech from its website, Nadeem said.

Phonic has raised $4 million in a seed round led by Lux with participation from Replit co-founder Amjad Masad, Hugging Face co-founder Clem Delangue, Applied Intuition co-founder Qasar Younis, and Modal Labs founder Erik Bernhardsson.

Grace Isford, a partner at Lux Capital, said that the company’s in-house way of training models was appealing to the investment firm.

“We think both Moin and Nikhil are incredible technologists,” he said. “They founded [a] machine learning club at MIT. And they have worked on training models for a while now. Plus, their approach of combining diffusion and proprietary models in the voice AI sector is novel.

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