13 citations · 23 across the 18 of their papers we have counts for
7 papers · 1 filter
Learning Spatially-Aware Language and Audio Embeddings
Bhavika Devnani, Skyler Seto, Zakaria Aldeneh +5
Humans can picture a sound scene given an imprecise natural language description. For example, it is easy to imagine an acoustic environment given a phrase like "the lion roar came…
Can you Remove the Downstream Model for Speaker Recognition with Self-Supervised Speech Features?
Zakaria Aldeneh, Takuya Higuchi, Jee-weon Jung +6
Self-supervised features are typically used in place of filter-bank features in speaker verification models. However, these models were originally designed to ingest filter-bank fe…
ESPnet-SPK: full pipeline speaker embedding toolkit with reproducible recipes, self-supervised front-ends, and off-the-shelf models
Jee-weon Jung, Wangyou Zhang, Jiatong Shi +5
This paper introduces ESPnet-SPK, a toolkit designed with several objectives for training speaker embedding extractors. First, we provide an open-source platform for researchers in…
Spatial LibriSpeech: An Augmented Dataset for Spatial Audio Learning
Miguel Sarabia, Elena Menyaylenko, Alessandro Toso +7
We present Spatial LibriSpeech, a spatial audio dataset with over 650 hours of 19-channel audio, first-order ambisonics, and optional distractor noise. Spatial LibriSpeech is desig…
On the role of Lip Articulation in Visual Speech Perception
Zakaria Aldeneh, Masha Fedzechkina, Skyler Seto +4
Generating realistic lip motion from audio to simulate speech production is critical for driving natural character animation. Previous research has shown that traditional metrics u…
MuSE-ing on the Impact of Utterance Ordering On Crowdsourced Emotion Annotations
Mimansa Jaiswal, Zakaria Aldeneh, Cristian-Paul Bara +4
Emotion recognition algorithms rely on data annotated with high quality labels. However, emotion expression and perception are inherently subjective. There is generally not a singl…
Capturing Long-term Temporal Dependencies with Convolutional Networks for Continuous Emotion Recognition
Soheil Khorram, Zakaria Aldeneh, Dimitrios Dimitriadis +2
The goal of continuous emotion recognition is to assign an emotion value to every frame in a sequence of acoustic features. We show that incorporating long-term temporal dependenci…