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Microsoft’s Neural text-to-speech tool now supports Hindi, Indian English

IANS
Updated: August 25th, 2020, 13:56 IST
in Sci-Tech
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Microsoft
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New Delhi: Microsoft India Tuesday announced the addition of English (India) and Hindi to its neural text-to-speech service (Neural TTS) language set.

The two are among the 15 new dialects added to the service, enabled with state-of-the-art AI audio quality.

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Microsoft’s Neural TTS can be used to make interactions with chatbots and virtual assistants more natural and engaging.

It is also being used to convert digital texts such as e-books into audiobooks and being deployed for in-car navigation systems, the company said in a statement.

“Through the inclusion of English (India) and Hindi in Neural TTS, we are demonstrating our continued commitment to refining speech and voice-based services for personal and business use in India,” said Sundar Srinivasan, General Manager, Microsoft India (R&D) Pvt Ltd.

Organisations in sectors like telecom, media and entertainment, retail, manufacturing, and product/ service development are using Neural TTS.

Udaan, India’s largest online business-to-business (B2B) marketplace, is using text-to-speech in Azure to develop conversational interfaces for their voice assistants, Microsoft said.

Neural TTS is a part of Microsoft Azure Cognitive Services and converts text to lifelike speech for a more natural interface.

The service also provides customisable voices, fine-tuned auto control, and flexible deployment from cloud to edge.

With natural-sounding speech that matches the stress patterns and intonation of human voices, Neural TTS significantly reduces listening fatigue when users are interacting with AI systems.

The service enables human-like natural and clear articulation and uses deep neural networks to overcome the limits of traditional text-to-speech systems in matching the patterns of stress and pitch in spoken language.

Overall, Microsoft TTS supports 110 voices and over 45 languages and variants.

Tags: HindiIndian EnglishMicrosoft
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