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Tokaido Shinkansen, one of Japan’s busiest and most popular bullet train lines, will now have AI-powered announcements from ReadSpeaker at its stations

May 30, 2023 by Gaea Vilage
Tokaido Shinkansen Train

Tokyo, Japan. May 25, 2023 – ReadSpeaker, the most trusted, independent digital voice partner for global businesses, today announced that Central Japan Railway Company (JR Central, head office at Nagoya, Aichi, President: Shunsuke Niwa) has implemented ReadSpeaker’s text-to-speech software for platform announcements at its train stations (Announcements specifically for train’s approach, arrival, and departure at platforms and concourses).

* Text-To-Speech (TTS) is a software that uses AI-powered voice synthesis technology to generate voice from text.

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Audio production for travel announcements made easier than ever

Travel announcements are generally recorded by professional voice actors. However, Additional recordings with voice talents are always costly and require the same voice actor to be available in order to ensure continuity. With speechMaker Desktop —ReadSpeaker’s AI voice authoring tool— it is now possible to generate text-to-speech travel announcements easily and automatically. 

Converts original audio into noise-resistant audio for improved audibility

On station platforms and concourses, announcements can be difficult to hear due to the noisy environment. This includes noise from moving trains, bad weather, and crowds of passengers. With speechMaker Desktop —ReadSpeaker’s AI voice authoring tool— it is now possible to easily and automatically generate text-to-speech travel announcements.  ReadSpeaker has become the first company in the world* to develop this function for TTS and utilize it in the transportation industry. Passengers can now hear announcements at stations more clearly than ever before.

This function has been researched and developed based on technology proposed by the Yamagishi Laboratory of the National Institute of Informatics in Japan. This technology provides clear voice sound in noisy environments.

*Based on ReadSpeaker research.

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