These new tools were provided to Alexa app developers in the form of a standardized markup language called Speech Synthesis Markup Language, or SSML, which will let them code Alexa's speech patterns into their applications.
Table 1 illustrates the kind of information provided to the general public on breast cancer screening: three plain language syntheses or fact boxes produced for individual decision-making on breast screening based on recent systematic reviews.
In recent years, voice-driven human-computer interaction has benefited greatly from steady improvements in the underlying speech technologies, such as speech recognition, speech synthesis, natural language understanding, and machine learning [1].
Behind the scenes, all of the telephony infrastructure, speech recognition, speech synthesis, natural language understanding and orchestration are managed automatically.
OpenSeq2Seq provides a large set of state-of-the-art models and building blocks for neural machine translation, automatic speech recognition, speech synthesis, and language modeling.
SSML is a markup language for speech synthesis applications, which allows you to make a text-to-speech voice more lifelike, adding breaks and pauses, tweaking vocal pitch, word rate etc.
Yes, the idea comes through in our compositions; we use an elaborate setup involving 8 to 10 laptops, an instrumented Wi-Fi network, statistical language models, speech synthesis software, and a synth-based on computer vision algorithms.
Results of perceptual experiments show that by integrating the steps of language generation and speech synthesis, we are able to achieve improved naturalness of synthetic speech compared to the sequential implementation.
The interfaces between consecutive modules conform to the speech synthesis markup language (SSML) specification for standardization, interoperability, multilinguality, and extensibility.
In this paper, we describe how language generation and speech synthesis for spoken dialog systems can be efficiently integrated under a weighted finite state transducer architecture.
These platforms offer more than a natural language interface (NLI): they demonstrate true advancements in combining a variety of emerging technologies -- everything from speech synthesis to natural language understanding (NLU) to cognitive and machine learning technologies -- and are capable of replacing humans in a variety of tasks.
