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Role of Voice Recognition Technology in Healthcare | by Shaip

24Mar

By ShaipNo Comments

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Role of Voice Recognition Technology in Healthcare | by Shaip

More and more people are using their voices to control their tech-enabled devices. From Siri to Alexa, users ask voice assistants to do mundane and complicated tasks. Healthcare providers are also finding creative ways to use voice recognition technology in the medical field to give patients more independence.Voice recognition technology allows doctors to use the right tools while busy with other tasks. It can also be used in home health care to ensure patients get the proper treatment. This blog will examine how voice recognition technology can be used in healthcare in 2022.Voice recognition is the ability to convert spoken words into written or electronic text. This technology can be used in various ways, including dictation and transcription services.AI is a branch of computer science d...

22Apr

By Dasha FominaNo Comments

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Conversational Commerce 2022: Trends and Drivers

Here’s a list of things that will dominate the conversational commerce industry in 2022.Image Credit: Polina PirogovaA recent report states that in 2021, consumers spent $871.03 billion online with U.S. merchants, which makes it a 14.2% rise YoY from $762.68 billion in 2020. The past two years of the COVID pandemic have fostered retail digitization and AI-powered conversational agents like chatbots and virtual assistants are among the main drivers. Juniper Research expects that chatbots will save retailers $439 million annually by 2023, while retail sales via conversational channels demonstrate a 98% annual growth rate and are projected to reach $142 billion by 2024. All in all, global spending on conversational commerce is predicted to grow nearly sevenfold to about $290 billion, by 2025....

“Speech Recognition” August 2021 — summary from Arxiv, Europe PMC and Springer Nature | by Brevi Assistant | Aug, 2021

30Aug

By Brevi AssistantNo Comments

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“Speech Recognition” August 2021 — summary from Arxiv, Europe PMC and Springer Nature | by Brevi Assistant | Aug, 2021

Arxiv — summary generated by Brevi AssistantIt’s challenging to personalize transducer-based automated speech recognition system with context information which is inaccessible and vibrant during version training. Experiments reveal that the design improves standard ASR model performance with about 50% relative word mistake rate decrease, which also substantially outperforms the baseline approach such as contextual LM biasing. In this paper, we provide AISHELL-4, a sizable real-recorded Mandarin speech dataset gathered by 8-channel round microphone selection for speech processing in conference scenario. Provided most open resource dataset for multi-speaker tasks are in English, AISHELL-4 is the only Mandarin dataset for conversation speech, supplying added worth for information diversity in...