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A stack of dilated casual convolutional layers used in WaveNet [1]. In September 2016, DeepMind proposed WaveNet, a deep generative model of raw audio waveforms, demonstrating that deep learning-based models are capable of modeling raw waveforms and generating speech from acoustic features like spectrograms or mel-spectrograms.
The Amazon Echo, an example of a voice computer. Voice computing is the discipline that develops hardware or software to process voice inputs. [1]It spans many other fields including human-computer interaction, conversational computing, linguistics, natural language processing, automatic speech recognition, speech synthesis, audio engineering, digital signal processing, cloud computing, data ...
Retrieval-based Voice Conversion (RVC) is an open source voice conversion AI algorithm that enables realistic speech-to-speech transformations, accurately preserving the intonation and audio characteristics of the original speaker.
Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition (ASR), computer speech recognition or speech-to-text (STT).
Each speaker recognition system has two phases: enrollment and verification. During enrollment, the speaker's voice is recorded and typically a number of features are extracted to form a voice print, template, or model. In the verification phase, a speech sample or "utterance" is compared against a previously created voice print.
The sampling resolution is typically 8 or more bits per sample resolution, for a data rate in the range of 64 kbit/s, but a good vocoder can provide a reasonably good simulation of voice with as little as 5 kbit/s of data. Toll quality voice coders, such as ITU G.729, are used in many telephone networks.
The system combines recorded voice samples with blacklists of repeat calls from would-be criminals, and has reduced fraud attempts by as much as 90 percent so far. And if you're wondering where ...
The Harvard sentences, or Harvard lines, [1] is a collection of 720 sample phrases, divided into lists of 10, used for standardized testing of Voice over IP, cellular, and other telephone systems. They are phonetically balanced sentences that use specific phonemes at the same frequency they appear in English.