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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.
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.
Technical writing is most commonly performed by a trained technical writer and the content they produce is the result of a well-defined process. Technical writers follow strict guidelines so the technical information they share appears in a single, popularly used and standardized format and style (e.g., DITA, markdown format, AP Stylebook, Chicago Manual of Style).
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.
An experienced voice therapist can quite reliably evaluate the voice, but this requires extensive training and is still subjective. Another active research topic in medical voice analysis is vocal loading evaluation. The vocal cords of a person who speaks for an extended time suffer from tiring—that is, the process of speaking exerts a load ...
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Speech processing is the study of speech signals and the processing methods of signals. The signals are usually processed in a digital representation, so speech processing can be regarded as a special case of digital signal processing, applied to speech signals.
PESQ is a full-reference algorithm and analyzes the speech signal sample-by-sample after a temporal alignment of corresponding excerpts of reference and test signal. PESQ can be applied to provide an end-to-end (E2E) quality assessment for a network, or characterize individual network components.