Which specific deep learning architecture, developed by DeepMind, famously generated high-fidelity speech by predicting one audio sample at a time?
Answer
WaveNet.
WaveNet represented a major breakthrough in Neural TTS (NTTS) by introducing a generative model capable of capturing extremely fine details in audio timing. Unlike preceding systems that modeled spectral features or stitched units, WaveNet predicted the value of the next audio sample sequentially, conditioning that prediction on all the audio samples that preceded it in the waveform. This autoregressive prediction method allowed the model to capture the micro-timing details and complex dependencies inherent in raw audio, resulting in synthesized audio with fidelity that was often nearly indistinguishable from real human recordings, though it initially required massive computational resources for training and generation.
Related Questions
How was the operation of the Voder, developed by Homer Dudley, primarily controlled when unveiled at the 1939 New York World's Fair?What acoustic principle underpinned the rule-based Formant Synthesis developed after the move to solid-state electronics in the 1960s?What inherent limitation often introduced audible artifacts into speech generated by Concatenative Synthesis methods?What process did the Vocoder, developed by Homer Dudley’s team after the Voder, perform on human speech input?Which specific deep learning architecture, developed by DeepMind, famously generated high-fidelity speech by predicting one audio sample at a time?In Concatenative Synthesis, what factor was directly dependent on the quality of the final voice output achieved by stitching segments?What statistical modeling tool formed the foundation of the Statistical Parametric Approach emerging in the late 1980s and 1990s?What crucial capability did the rudimentary acoustic devices like 'talking tubes' in the 18th and 19th centuries fundamentally lack compared to later synthesized speech?Based on the historical progression described, where did the burden of expertise shift when moving from operating the Voder to deploying modern Neural TTS systems?Which academic project originating at the University of Edinburgh was instrumental in developing toolkits like Flite and voice models such as 'C59' for concatenative synthesis research?