Search Results for author:
Found 7 papers, 2 papers with code
Toward a realistic model of speech processing in the brain with self-supervised learning
These elements, resulting from the largest neuroimaging benchmark to date, show how self-supervised learning can account for a rich organization of speech processing in the brain, and thus delineate a path to identify the laws of language acquisition which shape the human brain.
Predicting non-native speech perception using the Perceptual Assimilation Model and state-of-the-art acoustic models
Our native language influences the way we perceive speech sounds, affecting our ability to discriminate non-native sounds.
Do self-supervised speech models develop human-like perception biases?
We show that the CPC model shows a small native language effect, but that wav2vec 2. 0 and HuBERT seem to develop a universal speech perception space which is not language specific.
Inductive biases, pretraining and fine-tuning jointly account for brain responses to speech
Third, learning to process phonetically-related speech inputs (i. e., Dutch vs English) leads deep nets to reach higher levels of brain-similarity than learning to process phonetically-distant speech inputs (i. e. Dutch vs Bengali).
Perceptimatic: A human speech perception benchmark for unsupervised subword modelling
In this paper, we present a data set and methods to compare speech processing models and human behaviour on a phone discrimination task.
The Perceptimatic English Benchmark for Speech Perception Models
We show that DeepSpeech, a standard English speech recognizer, is more specialized on English phoneme discrimination than English listeners, and is poorly correlated with their behaviour, even though it yields a low error on the decision task given to humans.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Learning to detect dysarthria from raw speech
We extend this approach to paralinguistic classification and propose a neural network that can learn a filterbank, a normalization factor and a compression power from the raw speech, jointly with the rest of the architecture.
