Search Results for author:
Found 22 papers, 7 papers with code
How to Estimate Model Transferability of Pre-Trained Speech Models?
In this work, we introduce a ``score-based assessment'' framework for estimating the transferability of pre-trained speech models (PSMs) for fine-tuning target tasks.
Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages
We introduce the Universal Speech Model (USM), a single large model that performs automatic speech recognition (ASR) across 100+ languages.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Noise2Music: Text-conditioned Music Generation with Diffusion Models
We introduce Noise2Music, where a series of diffusion models is trained to generate high-quality 30-second music clips from text prompts.
From English to More Languages: Parameter-Efficient Model Reprogramming for Cross-Lingual Speech Recognition
In this work, we propose a new parameter-efficient learning framework based on neural model reprogramming for cross-lingual speech recognition, which can \textbf{re-purpose} well-trained English automatic speech recognition (ASR) models to recognize the other languages.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
A Quantum Kernel Learning Approach to Acoustic Modeling for Spoken Command Recognition
We propose a quantum kernel learning (QKL) framework to address the inherent data sparsity issues often encountered in training large-scare acoustic models in low-resource scenarios.
Residual Adapters for Few-Shot Text-to-Speech Speaker Adaptation
Adapting a neural text-to-speech (TTS) model to a target speaker typically involves fine-tuning most if not all of the parameters of a pretrained multi-speaker backbone model.
Maestro-U: Leveraging joint speech-text representation learning for zero supervised speech ASR
First, we show that by combining speech representations with byte-level text representations and use of language embeddings, we can dramatically reduce the Character Error Rate (CER) on languages with no supervised speech from 64. 8\% to 30. 8\%, a relative reduction of 53\%.
SpecGrad: Diffusion Probabilistic Model based Neural Vocoder with Adaptive Noise Spectral Shaping
Neural vocoder using denoising diffusion probabilistic model (DDPM) has been improved by adaptation of the diffusion noise distribution to given acoustic features.
A Comparative Study on Non-Autoregressive Modelings for Speech-to-Text Generation
Non-autoregressive (NAR) models simultaneously generate multiple outputs in a sequence, which significantly reduces the inference speed at the cost of accuracy drop compared to autoregressive baselines.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
WaveGrad 2: Iterative Refinement for Text-to-Speech Synthesis
The model takes an input phoneme sequence, and through an iterative refinement process, generates an audio waveform.
Focus on the present: a regularization method for the ASR source-target attention layer
This paper introduces a novel method to diagnose the source-target attention in state-of-the-art end-to-end speech recognition models with joint connectionist temporal classification (CTC) and attention training.
WaveGrad: Estimating Gradients for Waveform Generation
This paper introduces WaveGrad, a conditional model for waveform generation which estimates gradients of the data density.
Mask CTC: Non-Autoregressive End-to-End ASR with CTC and Mask Predict
In this work, Mask CTC model is trained using a Transformer encoder-decoder with joint training of mask prediction and CTC.
Audio and Speech Processing Sound
Robust Training of Vector Quantized Bottleneck Models
We show that the codebook learning can suffer from poor initialization and non-stationarity of clustered encoder outputs.
x-vectors meet emotions: A study on dependencies between emotion and speaker recognition
Then, we show the effect of emotion on speaker recognition.
Improving Language Identification for Multilingual Speakers
Spoken language identification (LID) technologies have improved in recent years from discriminating largely distinct languages to discriminating highly similar languages or even dialects of the same language.
Listen and Fill in the Missing Letters: Non-Autoregressive Transformer for Speech Recognition
In this paper, we study two different non-autoregressive transformer structure for automatic speech recognition (ASR): A-CMLM and A-FMLM.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Feature Enhancement with Deep Feature Losses for Speaker Verification
On BabyTrain corpus, we observe relative gains of 10. 38% and 12. 40% in minDCF and EER respectively.
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
A Comparative Study on Transformer vs RNN in Speech Applications
Sequence-to-sequence models have been widely used in end-to-end speech processing, for example, automatic speech recognition (ASR), speech translation (ST), and text-to-speech (TTS).
Ranked #7 on
Speech Recognition
on AISHELL-1
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
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).
ESPnet: End-to-End Speech Processing Toolkit
This paper introduces a new open source platform for end-to-end speech processing named ESPnet.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
