the Journal of the Acoustical Society of America

2010

Citations in the corpus

• Experience Grounds Language (2020, background): “This phenomenon has been noted count-less times, from Pierce (1969) criticizing Speech Recognition as “deceit and glamour” (Pierce, 1969) to Marcus and Davis (2019) which complains of humanity’s “gullibility gap”.”
• AI and the Everything in the Whole Wide World Benchmark (2021, background): “Pierce had written against AI more generally, and automated speech recognition in particular, deriding it as a waste of money and as impossible, given the contextual nature of speech [Pierce, 1969].”
• Almost Unsupervised Text to Speech and Automatic Speech Recognition (2019, methodology): “For TTS, we also evaluate the intelligibility of the voice (French & Steinberg, 1947) to verify if we can generate a reasonable speech sequence, considering few paired data are used.”
• Audio-Visual Speech Codecs: Rethinking Audio-Visual Speech Enhancement by Re-Synthesis (2022, background): “The ability of humans to isolate a target speaker from a noisy environment [41,57] has inspired extensive study into computational approaches for speech separation and enhancement.”
• Review of end-to-end speech synthesis technology based on deep learning (2021, methodology): “of TTS techniques from the formant-based methods [92, 97, 105, 106, 179, 224] to the unit selection-based waveform cascade methods [6, 24, 36, 59, 80, 143, 144], and to the hidden Markov model (HMM)-based statistical parametric speech synthesis (SPSS) methods [26, 94, 149, 178, 210, 241, 249, 250].”
• Lip Reading in the Wild (2016, methodology): “The Penn Phonetics Lab Forced Aligner [9, 41] (based on the open-source HTK toolbox [40]) is used to forcealign the subtitle to the audio signal.”
• Learning approach to optical tomography (2015, methodology): “The learning approach to imaging we describe in this paper is related to adaptive techniques in phased antenna arrays [1], iterative imaging schemes [2, 3], and inverse scattering [4, 5].”