Search Results for author:
Found 8 papers, 4 papers with code
The Zero Resource Speech Challenge 2017
We describe a new challenge aimed at discovering subword and word units from raw speech.
Sampling strategies in Siamese Networks for unsupervised speech representation learning
We apply these results to pairs of words discovered using an unsupervised algorithm and show an improvement on state-of-the-art in unsupervised representation learning using siamese networks.
The Zero Resource Speech Challenge 2019: TTS without T
We present the Zero Resource Speech Challenge 2019, which proposes to build a speech synthesizer without any text or phonetic labels: hence, TTS without T (text-to-speech without text).
Libri-Light: A Benchmark for ASR with Limited or No Supervision
Additionally, we provide baseline systems and evaluation metrics working under three settings: (1) the zero resource/unsupervised setting (ABX), (2) the semi-supervised setting (PER, CER) and (3) the distant supervision setting (WER).
Ranked #1 on
Speech Recognition
on Libri-Light test-other
(ABX-within metric)
The Zero Resource Speech Challenge 2020: Discovering discrete subword and word units
We present the Zero Resource Speech Challenge 2020, which aims at learning speech representations from raw audio signals without any labels.
Learning spectro-temporal representations of complex sounds with parameterized neural networks
We found out that models based on Learnable STRFs are on par for all tasks with different toplines, and obtain the best performance for Speech Activity Detection.
Shennong: a Python toolbox for audio speech features extraction
After describing the Shennong software architecture, its core components and implemented algorithms, this paper illustrates its use on three applications: a comparison of speech features performances on a phones discrimination task, an analysis of a Vocal Tract Length Normalization model as a function of the speech duration used for training and a comparison of pitch estimation algorithms under various noise conditions.
DP-Parse: Finding Word Boundaries from Raw Speech with an Instance Lexicon
Finding word boundaries in continuous speech is challenging as there is little or no equivalent of a 'space' delimiter between words.
