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WaveNet synthesizes more natural-sounding speech and, on average, produces speech audio that people prefer over other text-to-speech technologies. Rolling in the DeepMindIn addition, we're excited to announce that Cloud Text-to-Speech also includes a selection of high-fidelity voices built using WaveNet, a generative model for raw audio created by DeepMind. Cloud Text-to-Speech also allows you to customize pitch, speaking rate, and volume gain, and supports a variety of audio formats, including MP3 and WAV. Cloud Text-to-Speech correctly pronounces complex text such as names, dates, times and addresses for authentic sounding speech right out of the gate. To convert text-based media (e.g., news articles, books) into spoken format (e.g., podcast or audiobook)Ĭloud Text-to-Speech lets you choose from 32 different voices from 12 languages and variants.To enable IoT devices (e.g., TVs, cars, robots) to talk back to you.To power voice response systems for call centers (IVRs) and enabling real-time natural language conversations. You can use Cloud Text-to-Speech in a variety of ways, for example: Developers have been telling us they’d like to add text-to-speech to their own applications, so today we’re bringing this technology to Google Cloud Platform with Cloud Text-to-Speech. Such environments can be described as ‘real-like’ (or ‘verisimilar’), ‘abstract’, or ‘hybrid’, depending on the level to which they suggest the real world, and to which their constituent sources can be recognized, and also depending on the relationship between sounds and spaces with regard to recognition.Many Google products (e.g., the Google Assistant, Search, Maps) come with built-in high-quality text-to-speech synthesis that produces natural sounding speech. From a macro-perspective, and as can be concluded through the research, different ‘models’ of sound environments can be found within an acousmatic musical context. In the current research the aim was to create a rich variety of original ‘sound vocabulary’ overcoming basic problems existing in the recorded sound material (noise elimination, presence enhancement, event isolations, enhancement of tonality/sustainment) and further explore the transformational potential of the recorded and created sounds. Through the application of certain processing techniques, soundscapes may be transformed into different soundscapes or they may be decomposed to their constituents. To define these relationships through listening, one needs to consider a number of characteristics regarding sources and sound-behaviours and the manner in which these co-exist within the recorded soundscape. The soundscapes recorded in Corfu between 2006-2007 during the study of the area of lake Antinioti constitute an entity of different sound-environments often demonstrating antithetic relationships regarding the existence of sound-sources and sound-behaviours within their boundaries, as these recordings were captured in different times of day/night and in different seasons throughout the year. Soundscapes can also be an inspirational source, offering a number of sound-images and sound-behaviours to be approached mimetically, and thus to develop a certain ‘nature-oriented’ type of musical language. Soundscape recordings of nature may often provide us with a valuable sound-basis as a primary compositional material, offering a rich sound-world to transform and develop certain compositional strategies. |