8 citations · 15 across the 7 of their papers we have counts for
6 papers · 1 filter
Text-based Audio Retrieval by Learning from Similarities between Audio Captions
Huang Xie, Khazar Khorrami, Okko Räsänen +1
This paper proposes to use similarities of audio captions for estimating audio-caption relevances to be used for training text-based audio retrieval systems. Current audio-caption…
Integrating Continuous and Binary Relevances in Audio-Text Relevance Learning
Huang Xie, Khazar Khorrami, Okko Räsänen +1
Audio-text relevance learning refers to learning the shared semantic properties of audio samples and textual descriptions. The standard approach uses binary relevances derived from…
A model of early word acquisition based on realistic-scale audiovisual naming events
Khazar Khorrami, Okko Räsänen
Infants gradually learn to parse continuous speech into words and connect names with objects, yet the mechanisms behind development of early word perception skills remain unknown.…
Simultaneous or Sequential Training? How Speech Representations Cooperate in a Multi-Task Self-Supervised Learning System
Khazar Khorrami, María Andrea Cruz Blandón, Tuomas Virtanen +1
Speech representation learning with self-supervised algorithms has resulted in notable performance boosts in many downstream tasks. Recent work combined self-supervised learning (S…
Crowdsourcing and Evaluating Text-Based Audio Retrieval Relevances
Huang Xie, Khazar Khorrami, Okko Räsänen +1
This paper explores grading text-based audio retrieval relevances with crowdsourcing assessments. Given a free-form text (e.g., a caption) as a query, crowdworkers are asked to gra…
Can phones, syllables, and words emerge as side-products of cross-situational audiovisual learning? -- A computational investigation
Khazar Khorrami, Okko Räsänen
Decades of research has studied how language learning infants learn to discriminate speech sounds, segment words, and associate words with their meanings. While gradual development…