Basic syntax from speech: Spontaneous concatenation in unsupervised deep neural networks

• URL: http://arxiv.org/abs/2305.01626v2
• Date: Fri, 12 Jul 2024 10:30:23 GMT
• Title: Basic syntax from speech: Spontaneous concatenation in unsupervised deep neural networks
• Authors: Gašper Beguš, Thomas Lu, Zili Wang,
• Abstract summary: We propose that the most basic syntactic operations can be modeled directly from raw speech in a fully unsupervised way. 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.
• Score: 8.683116789109462
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Computational models of syntax are predominantly text-based. Here we propose that the most basic syntactic operations can be modeled directly from raw speech in a fully unsupervised way. We focus on one of the most ubiquitous and elementary properties 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. 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 from raw acoustic inputs.

Related papers

• Neural paraphrasing by automatically crawled and aligned sentence pairs [11.95795974003684]
  The main obstacle toward neural-network-based paraphrasing is the lack of large datasets with aligned pairs of sentences and paraphrases. We present a method for the automatic generation of large aligned corpora, that is based on the assumption that news and blog websites talk about the same events using different narrative styles. We propose a similarity search procedure with linguistic constraints that, given a reference sentence, is able to locate the most similar candidate paraphrases out from millions of indexed sentences.
  arXiv  Detail & Related papers  (2024-02-16T10:40:38Z)
• Audio-Visual Neural Syntax Acquisition [91.14892278795892]
  We study phrase structure induction from visually-grounded speech. We present the Audio-Visual Neural Syntax Learner (AV-NSL) that learns phrase structure by listening to audio and looking at images, without ever being exposed to text.
  arXiv  Detail & Related papers  (2023-10-11T16:54:57Z)
• Neural approaches to spoken content embedding [1.3706331473063877]
  We contribute new discriminative acoustic word embedding (AWE) and acoustically grounded word embedding (AGWE) approaches based on recurrent neural networks (RNNs) We apply our embedding models, both monolingual and multilingual, to the downstream tasks of query-by-example speech search and automatic speech recognition.
  arXiv  Detail & Related papers  (2023-08-28T21:16:08Z)
• RWEN-TTS: Relation-aware Word Encoding Network for Natural Text-to-Speech Synthesis [3.591224588041813]
  A huge number of text-to-speech (TTS) models produce human-like speech. Relation-aware Word Network (RWEN) effectively allows syntactic and semantic information based on two modules. Experimental results show substantial improvements compared to previous works.
  arXiv  Detail & Related papers  (2022-12-15T16:17:03Z)
• token2vec: A Joint Self-Supervised Pre-training Framework Using Unpaired Speech and Text [65.04385919645395]
  token2vec is a novel joint pre-training framework for unpaired speech and text based on discrete representations of speech. Experiments show that token2vec is significantly superior to various speech-only pre-training baselines, with up to 17.7% relative WER reduction.
  arXiv  Detail & Related papers  (2022-10-30T06:38:19Z)
• SpeechUT: Bridging Speech and Text with Hidden-Unit for Encoder-Decoder Based Speech-Text Pre-training [106.34112664893622]
  We propose a unified-modal speech-unit-text pre-training model, SpeechUT, to connect the representations of a speech encoder and a text decoder with a shared unit encoder. Our proposed SpeechUT is fine-tuned and evaluated on automatic speech recognition (ASR) and speech translation (ST) tasks.
  arXiv  Detail & Related papers  (2022-10-07T17:57:45Z)
• Wav2Seq: Pre-training Speech-to-Text Encoder-Decoder Models Using Pseudo Languages [58.43299730989809]
  We introduce Wav2Seq, the first self-supervised approach to pre-train both parts of encoder-decoder models for speech data. We induce a pseudo language as a compact discrete representation, and formulate a self-supervised pseudo speech recognition task. This process stands on its own, or can be applied as low-cost second-stage pre-training.
  arXiv  Detail & Related papers  (2022-05-02T17:59:02Z)
• Modeling speech recognition and synthesis simultaneously: Encoding and decoding lexical and sublexical semantic information into speech with no direct access to speech data [0.0]
  We introduce, to our knowledge, the most challenging objective in unsupervised lexical learning: an unsupervised network that must learn to assign unique representations for lexical items. Strong evidence in favor of lexical learning emerges. The architecture that combines the production and perception principles is thus able to learn to decode unique information from raw acoustic data in an unsupervised manner without ever accessing real training data.
  arXiv  Detail & Related papers  (2022-03-22T06:04:34Z)
• SPLAT: Speech-Language Joint Pre-Training for Spoken Language Understanding [61.02342238771685]
  Spoken language understanding requires a model to analyze input acoustic signal to understand its linguistic content and make predictions. Various pre-training methods have been proposed to learn rich representations from large-scale unannotated speech and text. We propose a novel semi-supervised learning framework, SPLAT, to jointly pre-train the speech and language modules.
  arXiv  Detail & Related papers  (2020-10-05T19:29:49Z)
• ALICE: Active Learning with Contrastive Natural Language Explanations [69.03658685761538]
  We propose Active Learning with Contrastive Explanations (ALICE) to improve data efficiency in learning. ALICE learns to first use active learning to select the most informative pairs of label classes to elicit contrastive natural language explanations. It extracts knowledge from these explanations using a semantically extracted knowledge.
  arXiv  Detail & Related papers  (2020-09-22T01:02:07Z)
• CiwGAN and fiwGAN: Encoding information in acoustic data to model lexical learning with Generative Adversarial Networks [0.0]
  Lexical learning is modeled as emergent from an architecture that forces a deep neural network to output data. Networks trained on lexical items from TIMIT learn to encode unique information corresponding to lexical items in the form of categorical variables in their latent space. We show that phonetic and phonological representations learned by the network can be productively recombined and directly paralleled to productivity in human speech.
  arXiv  Detail & Related papers  (2020-06-04T15:33:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.