Perceptimatic: A human speech perception benchmark for unsupervised subword modelling - Inria - Institut national de recherche en sciences et technologies du numérique
Conference Papers Year : 2020

Perceptimatic: A human speech perception benchmark for unsupervised subword modelling

Abstract

In this paper, we present a data set and methods to compare speech processing models and human behaviour on a phone discrimination task. We provide Perceptimatic, an open data set which consists of French and English speech stimuli, as well as the results of 91 English-and 93 French-speaking listeners. The stimuli test a wide range of French and English contrasts, and are extracted directly from corpora of natural running read speech, used for the 2017 Zero Resource Speech Challenge. We provide a method to compare humans' perceptual space with models' representational space, and we apply it to models previously submitted to the Challenge. We show that, unlike unsupervised models and supervised multilingual models, a standard supervised monolingual HMM-GMM phone recognition system, while good at discriminating phones, yields a representational space very different from that of human native listeners.
Fichier principal
Vignette du fichier
Interspeech_2020_Native_Perceptimatic__a_human_benchmark_for_the_Zerospeech_challenges.pdf (500.24 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-03087252 , version 1 (23-12-2020)

Identifiers

  • HAL Id : hal-03087252 , version 1

Cite

Juliette Millet, Ewan Dunbar. Perceptimatic: A human speech perception benchmark for unsupervised subword modelling. Interspeech 2020 - 21st Annual Conference of the International Speech Communication Association, Oct 2020, Shanghai / Virtual, China. ⟨hal-03087252⟩
62 View
71 Download

Share

More