Naturalizing and contextualizing a person’s communications with bots, or other intelligent voice response services has always been a challenge. However, in modern times, advances in cloud based cognitive services and data science strategies, such as representation methods and deep neural networks, have dramatically improved natural language processing. Applications across various industries are now capable of further optimizing dialogue-based services.
AI has been around since the early 1900’s. Artificial intelligence is everywhere, and cloud computing vendors have economically productized powerful AI features to drive market inclusion. AI improves how your apps understand, search, speak, hear, and see naturally. Cloud vendors like Microsoft deliver Azure Cognitive services, which enable developers to streamline AI service features into applications across all business verticals. This article will cover the Microsoft Azure cognitive service custom neural voice feature provided by the text-to-speech feature included in the Speech
Speech
Microsoft Azure Cognitive services provides a holistic speech service. The speech service includes recognition, agents, translation, transcription, speech-to-text, text-to-speech, device conversation and supports customization utilizing rich toolsets. The Speech services can be trained, tested, and deployed online or on premises using containers.
Text-to-Speech
The text-to-speech service changes text into natural sounding human speech. The principal features include visemes, which are observations of tongue, jaw, and lip positions to help create faces in lip reading services. You can modify the sounds being emitted with Speech Syntheses Markup Language, and support lengthy audio text-to-speech files.
Neural Voice
Microsoft Azure’s cognitive service known as Speech can be modified to generate synthesized human-like speech and deliver a personalized multi-communication experience. It accomplishes this by using deep learning, which is a subset of machine learning. Using Speech, you train neural TTS models by uploading audio files and scripts, that you can then test and ultimately deploy.
