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Found 23 papers, 13 papers with code
MAD Speech: Measures of Acoustic Diversity of Speech
Using these datasets, we demonstrate that our proposed metrics achieve a stronger agreement with the ground-truth diversity than baselines.
AudioPaLM: A Large Language Model That Can Speak and Listen
AudioPaLM inherits the capability to preserve paralinguistic information such as speaker identity and intonation from AudioLM and the linguistic knowledge present only in text large language models such as PaLM-2.
Long-term Effects of Temperature Variations on Economic Growth: A Machine Learning Approach
This study investigates the long-term effects of temperature variations on economic growth using a data-driven approach.
SoundStorm: Efficient Parallel Audio Generation
We present SoundStorm, a model for efficient, non-autoregressive audio generation.
AudioLM: a Language Modeling Approach to Audio Generation
We introduce AudioLM, a framework for high-quality audio generation with long-term consistency.
Generative Spoken Dialogue Language Modeling
We introduce dGSLM, the first "textless" model able to generate audio samples of naturalistic spoken dialogues.
textless-lib: a Library for Textless Spoken Language Processing
Textless spoken language processing research aims to extend the applicability of standard NLP toolset onto spoken language and languages with few or no textual resources.
Textless Speech Emotion Conversion using Decomposed & Discrete Representations
We decompose speech into discrete and disentangled learned representations, consisting of content units, F0, speaker, and emotion.
Textless Speech Emotion Conversion using Discrete and Decomposed Representations
We use a decomposition of the speech signal into discrete learned representations, consisting of phonetic-content units, prosodic features, speaker, and emotion.
How BPE Affects Memorization in Transformers
In this work, we demonstrate that the size of the subword vocabulary learned by Byte-Pair Encoding (BPE) greatly affects both ability and tendency of standard Transformer models to memorize training data, even when we control for the number of learned parameters.
Text-Free Prosody-Aware Generative Spoken Language Modeling
Generative Spoken Language Modeling (GSLM) \cite{Lakhotia2021} is the only prior work addressing the generative aspects of speech pre-training, which replaces text with discovered phone-like units for language modeling and shows the ability to generate meaningful novel sentences.
Can Transformers Jump Around Right in Natural Language? Assessing Performance Transfer from SCAN
We present several focused modifications of Transformer that greatly improve generalization capabilities on SCAN and select one that remains on par with a vanilla Transformer on a standard machine translation (MT) task.
Interpretable agent communication from scratch (with a generic visual processor emerging on the side)
As deep networks begin to be deployed as autonomous agents, the issue of how they can communicate with each other becomes important.
The Zero Resource Speech Challenge 2021: Spoken language modelling
We present the Zero Resource Speech Challenge 2021, which asks participants to learn a language model directly from audio, without any text or labels.
Speech Resynthesis from Discrete Disentangled Self-Supervised Representations
We propose using self-supervised discrete representations for the task of speech resynthesis.
Data Augmenting Contrastive Learning of Speech Representations in the Time Domain
Contrastive Predictive Coding (CPC), based on predicting future segments of speech based on past segments is emerging as a powerful algorithm for representation learning of speech signal.
What they do when in doubt: a study of inductive biases in seq2seq learners
Sequence-to-sequence (seq2seq) learners are widely used, but we still have only limited knowledge about what inductive biases shape the way they generalize.
Compositionality and Generalization in Emergent Languages
Third, while compositionality is not necessary for generalization, it provides an advantage in terms of language transmission: The more compositional a language is, the more easily it will be picked up by new learners, even when the latter differ in architecture from the original agents.
Emergent Language Generalization and Acquisition Speed are not tied to Compositionality
Studies of discrete languages emerging when neural agents communicate to solve a joint task often look for evidence of compositional structure.
EGG: a toolkit for research on Emergence of lanGuage in Games
There is renewed interest in simulating language emergence among deep neural agents that communicate to jointly solve a task, spurred by the practical aim to develop language-enabled interactive AIs, as well as by theoretical questions about the evolution of human language.
Entropy Minimization In Emergent Languages
There is growing interest in studying the languages that emerge when neural agents are jointly trained to solve tasks requiring communication through a discrete channel.
Anti-efficient encoding in emergent communication
Despite renewed interest in emergent language simulations with neural networks, little is known about the basic properties of the induced code, and how they compare to human language.
Word-order biases in deep-agent emergent communication
We train models to communicate about paths in a simple gridworld, using miniature languages that reflect or violate various natural language trends, such as the tendency to avoid redundancy or to minimize long-distance dependencies.
