Basic syntax from speech: Spontaneous concatenation in unsupervised deep neural networks (2305.01626v3)

Abstract: Computational models of syntax are predominantly text-based. Here we propose that the most basic first step in the evolution of syntax can be modeled directly from raw speech in a fully unsupervised way. We focus on one of the most ubiquitous and elementary suboperation of syntax -- concatenation. We introduce spontaneous concatenation: a phenomenon where convolutional neural networks (CNNs) trained on acoustic recordings of individual words start generating outputs with two or even three words concatenated without ever accessing data with multiple words in the input. We replicate this finding in several independently trained models with different hyperparameters and training data. Additionally, networks trained on two words learn to embed words into novel unobserved word combinations. We also show that the concatenated outputs contain precursors to compositionality. To our knowledge, this is a previously unreported property of CNNs trained in the ciwGAN/fiwGAN setting on raw speech and has implications both for our understanding of how these architectures learn as well as for modeling syntax and its evolution in the brain from raw acoustic inputs. We also propose a potential neural mechanism called disinhibition that outlines a possible neural pathway towards concatenation and compositionality and suggests our modeling is useful for generating testable prediction for biological and artificial neural processing of speech.

• The paper demonstrates unsupervised DNNs' ability to learn basic syntax via spontaneous concatenation from raw speech signals.
• It employs CNN-based GAN architectures, notably ciwGAN and fiwGAN, to map latent codes to specific lexical items.
• Results show that manipulating latent codes yields novel multi-word constructs, informing advancements in speech-driven language modeling.

Insights on Syntax Modeling from Raw Speech with Unsupervised Deep Learning

The paper "Basic syntax from speech: Spontaneous concatenation in unsupervised deep neural networks" by Beguš, Lu, and Wang tackles a pioneering approach to model basic syntactic properties directly from raw speech data using unsupervised deep neural networks, particularly focusing on convolutional neural networks (CNNs) and Generative Adversarial Networks (GANs). The central inquiry revolves around the ability of these models to spontaneously exhibit the property of concatenation, thus modeling basic syntax without recourse to text-based data.

Key Concepts and Methodology

The research primarily utilizes CNNs within the GAN setting, an architecture referred to as ciwGAN and fiwGAN, to explore the phenomenon termed "spontaneous concatenation." This phenomenon is defined by the networks' ability to generate outputs comprising concatenated words without being exposed to such combinations in their input data. The experimental design involved training the networks strictly on single-word inputs from the TIMIT database, with a selected group of lexical items subject to varying configurations across different network settings.

The networks undergo a rigorous training process where the Generative component of the GAN is conditioned not only to deceive the Discriminator but also to maximize mutual information between the latent space and observed data. Through strategic manipulation of latent codes—both within the range encountered during training and beyond—the paper investigates the networks' capabilities to generate novel word concatenations and linguistic features reflective of syntactic learning.

Notable Findings and Results

The paper presents several significant findings:

1. Emergence of Lexical and Syntactic Patterns: The networks displayed a robust ability to associate latent codes with specific lexical items, achieving near-categorical outputs on manipulated codes.
2. Spontaneous Concatenation: Despite the absence of concatenated word inputs during training, the models exhibited spontaneous concatenation of words when latent codes were adjusted to non-standard values. Remarkably, negative latent values frequently resulted in concatenated sequences, suggesting latent space utilization for uncovered syntactic constructions.
3. Potential for Multi-Word Constructs: In an experimental extension involving two-word training inputs, networks demonstrated the capacity to derive and encode unobserved combinations and occasionally, sequences extending to three-word constructs. Furthermore, instances of repetitive outputs mimicked processes resembling reduplication in linguistic structures.

Implications and Prospects

The paper accentuates the validity of unsupervised deep learning frameworks in modeling foundational syntax properties directly from speech signals. This decreases dependency on textual intermediaries and offers nuanced perspectives on language acquisition and synthesis directly from auditory data. The spontaneous concatenation observed suggests potential pathways for unsupervised models to tackle more sophisticated syntactic constructs, imperatively informing both linguistic theory and practical applications in speech processing technologies.

Looking towards the future, this research provides grounding for further exploration into the syntactic capabilities of unsupervised neural networks. The capacity to model speech-driven syntax could revolutionize understanding of language evolution and development, inform architectural enhancements in neural network design, and foster advancements in directly processing spoken language for various AI applications.

This paper lays foundational work that beckons further inquiry into more complex syntactic phenomena and their computational modeling from acoustical data, potentially redefining how AI systems perceive and generate natural language.