Self-supervised speech representation learning: A review
Although supervised deep learning has revolutionized speech and audio processing, it has
necessitated the building of specialist models for individual tasks and application scenarios …
necessitated the building of specialist models for individual tasks and application scenarios …
[HTML][HTML] Unsupervised automatic speech recognition: A review
Abstract Automatic Speech Recognition (ASR) systems can be trained to achieve
remarkable performance given large amounts of manually transcribed speech, but large …
remarkable performance given large amounts of manually transcribed speech, but large …
Effectiveness of self-supervised pre-training for speech recognition
We compare self-supervised representation learning algorithms which either explicitly
quantize the audio data or learn representations without quantization. We find the former to …
quantize the audio data or learn representations without quantization. We find the former to …
The zero resource speech challenge 2017
E Dunbar, XN Cao, J Benjumea… - 2017 IEEE Automatic …, 2017 - ieeexplore.ieee.org
We describe a new challenge aimed at discovering subword and word units from raw
speech. This challenge is the followup to the Zero Resource Speech Challenge 2015. It …
speech. This challenge is the followup to the Zero Resource Speech Challenge 2015. It …
Effectiveness of self-supervised pre-training for asr
We compare self-supervised representation learning algorithms which either explicitly
quantize the audio data or learn representations without quantization. We find the former to …
quantize the audio data or learn representations without quantization. We find the former to …
Cognitive science in the era of artificial intelligence: A roadmap for reverse-engineering the infant language-learner
E Dupoux - Cognition, 2018 - Elsevier
Spectacular progress in the information processing sciences (machine learning, wearable
sensors) promises to revolutionize the study of cognitive development. Here, we analyse the …
sensors) promises to revolutionize the study of cognitive development. Here, we analyse the …
Evaluating speech features with the minimal-pair ABX task: Analysis of the classical MFC/PLP pipeline
We present a new framework for the evaluation of speech rep-resentations in zero-resource
settings, that extends and complements previous work by Carlin, Jansen and Hermansky [1] …
settings, that extends and complements previous work by Carlin, Jansen and Hermansky [1] …
A segmental framework for fully-unsupervised large-vocabulary speech recognition
Zero-resource speech technology is a growing research area that aims to develop methods
for speech processing in the absence of transcriptions, lexicons, or language modelling text …
for speech processing in the absence of transcriptions, lexicons, or language modelling text …
Self-supervised language learning from raw audio: Lessons from the zero resource speech challenge
E Dunbar, N Hamilakis… - IEEE Journal of Selected …, 2022 - ieeexplore.ieee.org
Recent progress in self-supervised or unsupervised machine learning has opened the
possibility of building a full speech processing system from raw audio without using any …
possibility of building a full speech processing system from raw audio without using any …
Word segmentation on discovered phone units with dynamic programming and self-supervised scoring
H Kamper - IEEE/ACM Transactions on Audio, Speech, and …, 2022 - ieeexplore.ieee.org
Recent work on unsupervised speech segmentation has used self-supervised models with
phone and word segmentation modules that are trained jointly. This paper instead revisits …
phone and word segmentation modules that are trained jointly. This paper instead revisits …