Speech recognition

…of these techniques—to machine vision, speech recognition, autonomous vehicles, natural language, and many other domains—are coming so quickly that many observers predict that the long-elusive goal of “Artificial General Intelligence” (AGI) is within our grasp. However, we still cannot build a machine capable of building a nest, stalking prey, or loading a dishwasher. I will describe how evolution may have shaped the algor

Our ability to perceive speech remains resilient to variations in voice, rate, and temporal interruptions. While inference models addressed the first two [1], the computational principles by which speech understanding remains impervious to temporal restructuring are largely unexplored. Previous studies indicated intriguing recoveries in comprehension when speech is temporally modulated [2-4]. We show that predictive coding, constrained by endogenous rhythms, accounts for these quizzical results and enables robust lexical speech recovery. We build upon the major hypotheses that the rhythmic st

Speech processing, with its temporal cadence and multi-scale syntactic invariants (syllables, words), is a paradigmatic example where rhythms have been proposed to play a key role [1], with a speech-modulated hierarchical structure of intercoupled cortical oscillations correlating with successful comprehension. Experiments show that speech recognition remains largely intact when compressed up to a certain temporal factor [2] and the re-spacing of chunks of incomprehensible compressed speech with silences recovers comprehension. We hypothesize that rhythm-based top-down semantic context inferen

…Speech processing, with its temporal cadence and multi-scale of syntactic invariants (syllables, words), is a paradigmatic example where rhythms have been proposed to play a key role [1], with a speech-modulated hierarchical structure of intercoupled cortical oscillations correlating with successful comprehens

The human auditory system is robust to many types of corrupting noise. However, the neural mechanism that drives this robustness is unclear. Experimental evidence suggests that top-down predictions play an important role in processing stimuli in noisy contexts. Feedback connections are abundant in the auditory cortex, and have been hypothesized to carry predictive signals but are often omitted in large scale models. We evaluate the potential role of predictive dynamics in auditory robustness by introducing these dynamics into a large-scale model of the human auditory system. Specifically, we a

…can cause severe difficulties for speech recognition algorithms and people with hearing impairments. This study examines how the auditory system copes with reverberation. We trained a linear model to recover a rich set of natural, anechoic sounds from their simulated reverberant cou