![]() The Transformer TTS model is based on the auto-regressive Transformer structure, which can produce speech output in the quality close to the actual human voices with 5x less training time. Inspired by the Transformer model-a powerful sequence-to-sequence modeling architecture that advanced the state-of-the-art in neural machine translation (NMT), Microsoft researchers piloted the Transformer and FastSpeech models on Neural TTS and saw significant improvements in performance and efficiency. ![]() Neural TTS initially achieved near-human-parity on sentence reading using a recurrent neural network (RNN) based sequence-to-sequence model. Voice quality and performance improved with state-of-the-art neural speech synthesis models See the full language list for Neural TTS and standard voices. According to several MOS tests we have done (n>50 for each study), the average MOS score for the 15 new Neural TTS voices is above 4.1, about +0.5 higher than the scores for standard (non-neural) voices. For MOS studies, participants rate speech characteristics such as sound quality, pronunciation, speaking rate, and articulation on a 5-point scale. Text-to-speech quality is measured by Mean Opinion Score (MOS), a widely-recognized scoring method for speech quality evaluation. Wyjazd z Poznania planujemy o godzinie czwartej rano.Īmanhã vai estar tanto calor que vou à praia. Tavoitteena on lisätä kohtuuhintaisten vuokra-asuntojen määrää kasvukeskuksissa.Īlle oceanen zijn met elkaar verbonden en vormen samen één grote massa zout water. How about coming to the barbecue at the tennis club? Halvfjerds procent af din krop består af vand L'obra és el retrat d'un moment històric de mobilització popular. Hear samples of the voices, or try them with your own text in our demo. Our new Neural TTS voices include: Salma in Arabic (Egypt), Zariyah in Arabic (Saudi Arabia), Alba in Catalan (Spain), Christel in Danish (Denmark), Neerja in English (India), Noora in Finnish (Finland), Swara in Hindi (India), Colette in Dutch (Netherland), Zofia in Polish (Poland), Fernanda in Portuguese (Portugal), Dariya in Russian (Russia), Hillevi in Swedish (Sweden), Achara in Thai (Thailand), HiuGaai in Chinese (Cantonese, Traditional) and HsiaoYu in Chinese (Taiwanese Mandarin). Language support extended with 15 new voices ![]() To make it possible for more developers to add natural-sounding voices to their applications and solutions, today, we’re building on our language support with 15 new Neural TTS voices along with significant voice quality improvements. Companies like the BBC and Motorola Solutions are using Text to Speech in Azure to develop conversational interfaces for their voice assistants. Neural Text to Speech (Neural TTS) enables a wide range of scenarios, from audio content creation to natural-sounding voice assistants. Neural Text to Speech , part of Speech in Azure Cognitive Services, enables you to convert text to lifelike speech for more natural interfaces. ![]() This post was co-authored by Sheng Zhao, Jie Ding, Anny Dow, Garfield He and Lei He. ![]() Neural Text to Speech extends support to 15 more languages with state-of-the-art AI quality ![]()
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