The official edited transcript of what goes on in Parliament is published daily and details both the momentous occasions and the quieter moments in the Commons. Daily Politics reporter Ellie Price met some of the Hansard team - Vivien Wilson, Helen Lowe and Jack Homer - who hear, and publish, every word that is said in Parliament.
"It has been a great honor and a privilege to serve as your governor. Traveling the state, I have talked to many of you who harbor extraordinary anger at this ordeal and for those who have pushed and promoted it. For those who would be moved to vengeance, let us allow history and God to bring justice.
Voice assistants like Alexa convert written words into speech using text-to-speech systems, the most capable of which tap AI to verbalize from scratch rather than stringing together prerecorded snippets of sounds. Neural text-to-speech systems, or NTTS, tend to produce more natural-sounding speech than conventional models, but arguably their real value lies in their adaptability, as they're able to mimic the prosody of a recording, or its shifts in tempo, pitch, and volume. In a paper ("Fine-Grained Robust Prosody Transfer for Single-Speaker Neural Text-to-Speech") presented at this year's Interspeech conference in Graz, Austria, Amazon scientists investigated prosody transfer with a system that enabled them to choose voices in recordings while preserving the original inflections. They say it significantly improved on past attempts, which generally haven't adapted well to input voices they haven't encountered before. To this end, the team's system leveraged prosodic features that are easier to normalize than the raw spectrograms (representations of changes in signal frequency over time) typically ingested by neural text-to-speech networks.
In this century, a new phrase has entered the language to describe the accommodation of a new and undesirable order, that phrase being the "new normal." That we must never adjust to the present coarseness of our national dialogue, with the tone set at the top. We must never regard as normal the regular and casual undermining of our democratic norms and ideals. We must never meekly accept the daily sundering of our country. The personal attacks, the threats against principles, freedoms and institution, the flagrant disregard for truth and decency, the reckless provocations, most often for the pettiest and most personal reasons, reasons having nothing whatsoever to do with the fortunes of the people that we have been elected to serve.
This paper investigates incremental part of speech tagging for speech transcripts that contain multilin- gual intrasentential code-mixing, and compares the accuracy of a monolithic tagging model trained on a heterogeneous-language dataset to a model that switches between two homogeneous-language tagging models dynamically using word-by-word language identification. We find that the dynamic model, even though presented a smaller context consisting of sen- tence fragments, meets the accuracy of the monolithic code-mixing model which is aware of increased context. Our system is modular, and is designed to be expanded to many-language code-mixing.