Search Results for author:
Found 18 papers, 8 papers with code
Adaptive Accompaniment with ReaLchords
Jamming requires coordination, anticipation, and collaborative creativity between musicians.
Zero-shot Cross-lingual Voice Transfer for TTS
In this paper, we introduce a zero-shot Voice Transfer (VT) module that can be seamlessly integrated into a multi-lingual Text-to-speech (TTS) system to transfer an individual's voice across languages.
Adversarial training of Keyword Spotting to Minimize TTS Data Overfitting
The keyword spotting (KWS) problem requires large amounts of real speech training data to achieve high accuracy across diverse populations.
Extending Multilingual Speech Synthesis to 100+ Languages without Transcribed Data
Without any transcribed speech in a new language, this TTS model can generate intelligible speech in >30 unseen languages (CER difference of <10% to ground truth).
High-precision Voice Search Query Correction via Retrievable Speech-text Embedings
In this paper, we eliminate the hypothesis-audio mismatch problem by querying the correction database directly using embeddings derived from the utterance audio; the embeddings of the utterance audio and candidate corrections are produced by multimodal speech-text embedding networks trained to place the embedding of the audio of an utterance and the embedding of its corresponding textual transcript close together.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Understanding Shared Speech-Text Representations
Recently, a number of approaches to train speech models by incorpo-rating text into end-to-end models have been developed, with Mae-stro advancing state-of-the-art automatic speech recognition (ASR)and Speech Translation (ST) performance.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
R-MelNet: Reduced Mel-Spectral Modeling for Neural TTS
This paper introduces R-MelNet, a two-part autoregressive architecture with a frontend based on the first tier of MelNet and a backend WaveRNN-style audio decoder for neural text-to-speech synthesis.
MIDI-DDSP: Detailed Control of Musical Performance via Hierarchical Modeling
Musical expression requires control of both what notes are played, and how they are performed.
Planning in Dynamic Environments with Conditional Autoregressive Models
We demonstrate the use of conditional autoregressive generative models (van den Oord et al., 2016a) over a discrete latent space (van den Oord et al., 2017b) for forward planning with MCTS.
Harmonic Recomposition using Conditional Autoregressive Modeling
We demonstrate a conditional autoregressive pipeline for efficient music recomposition, based on methods presented in van den Oord et al.(2017).
Representation Mixing for TTS Synthesis
Recent character and phoneme-based parametric TTS systems using deep learning have shown strong performance in natural speech generation.
Blindfold Baselines for Embodied QA
We explore blindfold (question-only) baselines for Embodied Question Answering.
Learning Distributed Representations from Reviews for Collaborative Filtering
However, interestingly, the greater modeling power offered by the recurrent neural network appears to undermine the model's ability to act as a regularizer of the product representations.
Learning to Discover Sparse Graphical Models
Learning this function brings two benefits: it implicitly models the desired structure or sparsity properties to form suitable priors, and it can be tailored to the specific problem of edge structure discovery, rather than maximizing data likelihood.
ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation
Moreover, ReNet layers are stacked on top of pre-trained convolutional layers, benefiting from generic local features.
Ranked #20 on
Semantic Segmentation
on CamVid
A Recurrent Latent Variable Model for Sequential Data
In this paper, we explore the inclusion of latent random variables into the dynamic hidden state of a recurrent neural network (RNN) by combining elements of the variational autoencoder.
ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks
In this paper, we propose a deep neural network architecture for object recognition based on recurrent neural networks.
