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Found 13 papers, 9 papers with code
A Large-Scale Evaluation of Speech Foundation Models
In this work, we establish the Speech processing Universal PERformance Benchmark (SUPERB) to study the effectiveness of the paradigm for speech.
Cascading and Direct Approaches to Unsupervised Constituency Parsing on Spoken Sentences
The goal is to determine the spoken sentences' hierarchical syntactic structure in the form of constituency parse trees, such that each node is a span of audio that corresponds to a constituent.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Losses Can Be Blessings: Routing Self-Supervised Speech Representations Towards Efficient Multilingual and Multitask Speech Processing
We believe S$^3$-Router has provided a new perspective for practical deployment of speech SSL models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
ContentVec: An Improved Self-Supervised Speech Representation by Disentangling Speakers
Self-supervised learning in speech involves training a speech representation network on a large-scale unannotated speech corpus, and then applying the learned representations to downstream tasks.
Conditioned Natural Language Generation using only Unconditioned Language Model: An Exploration
Transformer-based language models have shown to be very powerful for natural language generation (NLG).
Towards Semi-Supervised Semantics Understanding from Speech
Much recent work on Spoken Language Understanding (SLU) falls short in at least one of three ways: models were trained on oracle text input and neglected the Automatics Speech Recognition (ASR) outputs, models were trained to predict only intents without the slot values, or models were trained on a large amount of in-house data.
Semi-Supervised Spoken Language Understanding via Self-Supervised Speech and Language Model Pretraining
Much recent work on Spoken Language Understanding (SLU) is limited in at least one of three ways: models were trained on oracle text input and neglected ASR errors, models were trained to predict only intents without the slot values, or models were trained on a large amount of in-house data.
Can Speaker Augmentation Improve Multi-Speaker End-to-End TTS?
This is followed by an analysis on synthesis quality, speaker and dialect similarity, and a remark on the effectiveness of our speaker augmentation approach.
Zero-Shot Multi-Speaker Text-To-Speech with State-of-the-art Neural Speaker Embeddings
While speaker adaptation for end-to-end speech synthesis using speaker embeddings can produce good speaker similarity for speakers seen during training, there remains a gap for zero-shot adaptation to unseen speakers.
Audio and Speech Processing
Contrastive Predictive Coding Based Feature for Automatic Speaker Verification
This thesis describes our ongoing work on Contrastive Predictive Coding (CPC) features for speaker verification.
ASSERT: Anti-Spoofing with Squeeze-Excitation and Residual neTworks
We present JHU's system submission to the ASVspoof 2019 Challenge: Anti-Spoofing with Squeeze-Excitation and Residual neTworks (ASSERT).
Attentive Filtering Networks for Audio Replay Attack Detection
In this work, we propose our replay attacks detection system - Attentive Filtering Network, which is composed of an attention-based filtering mechanism that enhances feature representations in both the frequency and time domains, and a ResNet-based classifier.
