Search Results for author:
Found 13 papers, 6 papers with code
Self-supervised Adaptive Pre-training of Multilingual Speech Models for Language and Dialect Identification
To address this challenge, we propose self-supervised adaptive pre-training (SAPT) to adapt the pre-trained model to the target domain and languages of the downstream task.
An Information-Theoretic Analysis of Self-supervised Discrete Representations of Speech
Self-supervised representation learning for speech often involves a quantization step that transforms the acoustic input into discrete units.
Developing the Reliable Shallow Supervised Learning for Thermal Comfort using ASHRAE RP-884 and ASHRAE Global Thermal Comfort Database II
This work introduces the reliable data set for training the AI subsystem for thermal comfort.
Analyzing the Representational Geometry of Acoustic Word Embeddings
In this paper, we take a closer analytical look at AWEs learned from English speech and study how the choice of the learning objective and the architecture shapes their representational profile.
Integrating Form and Meaning: A Multi-Task Learning Model for Acoustic Word Embeddings
Models of acoustic word embeddings (AWEs) learn to map variable-length spoken word segments onto fixed-dimensionality vector representations such that different acoustic exemplars of the same word are projected nearby in the embedding space.
How Familiar Does That Sound? Cross-Lingual Representational Similarity Analysis of Acoustic Word Embeddings
We further discuss the implications of our work on modeling speech processing and language similarity with neural networks.
Do Acoustic Word Embeddings Capture Phonological Similarity? An Empirical Study
Our experiments show that (1) the distance in the embedding space in the best cases only moderately correlates with phonological distance, and (2) improving the performance on the word discrimination task does not necessarily yield models that better reflect word phonological similarity.
SIGTYP 2021 Shared Task: Robust Spoken Language Identification
While language identification is a fundamental speech and language processing task, for many languages and language families it remains a challenging task.
Do we read what we hear? Modeling orthographic influences on spoken word recognition
Theories and models of spoken word recognition aim to explain the process of accessing lexical knowledge given an acoustic realization of a word form.
Familiar words but strange voices: Modelling the influence of speech variability on word recognition
We present a deep neural model of spoken word recognition which is trained to retrieve the meaning of a word (in the form of a word embedding) given its spoken form, a task which resembles that faced by a human listener.
A Closer Look at Linguistic Knowledge in Masked Language Models: The Case of Relative Clauses in American English
Transformer-based language models achieve high performance on various tasks, but we still lack understanding of the kind of linguistic knowledge they learn and rely on.
Rediscovering the Slavic Continuum in Representations Emerging from Neural Models of Spoken Language Identification
In this paper, we present a neural model for Slavic language identification in speech signals and analyze its emergent representations to investigate whether they reflect objective measures of language relatedness and/or non-linguists' perception of language similarity.
Cross-Domain Adaptation of Spoken Language Identification for Related Languages: The Curious Case of Slavic Languages
State-of-the-art spoken language identification (LID) systems, which are based on end-to-end deep neural networks, have shown remarkable success not only in discriminating between distant languages but also between closely-related languages or even different spoken varieties of the same language.
