Establishing degrees of closeness between audio recordings along different dimensions using large-scale cross-lingual models

• URL: http://arxiv.org/abs/2402.05581v1
• Date: Thu, 8 Feb 2024 11:31:23 GMT
• Title: Establishing degrees of closeness between audio recordings along different dimensions using large-scale cross-lingual models
• Authors: Maxime Fily, Guillaume Wisniewski, Severine Guillaume, Gilles Adda, Alexis Michaud
• Abstract summary: We propose a new unsupervised method using ABX tests on audio recordings with carefully curated metadata. Three experiments are devised: one on room acoustics aspects, one on linguistic genre, and one on phonetic aspects. The results confirm that the representations extracted from recordings with different linguistic/extra-linguistic characteristics differ along the same lines.
• Score: 4.349838917565205
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In the highly constrained context of low-resource language studies, we explore vector representations of speech from a pretrained model to determine their level of abstraction with regard to the audio signal. We propose a new unsupervised method using ABX tests on audio recordings with carefully curated metadata to shed light on the type of information present in the representations. ABX tests determine whether the representations computed by a multilingual speech model encode a given characteristic. Three experiments are devised: one on room acoustics aspects, one on linguistic genre, and one on phonetic aspects. The results confirm that the representations extracted from recordings with different linguistic/extra-linguistic characteristics differ along the same lines. Embedding more audio signal in one vector better discriminates extra-linguistic characteristics, whereas shorter snippets are better to distinguish segmental information. The method is fully unsupervised, potentially opening new research avenues for comparative work on under-documented languages.

Related papers

• Towards hate speech detection in low-resource languages: Comparing ASR to acoustic word embeddings on Wolof and Swahili [16.424308444697015]
  We consider hate speech detection through keyword spotting on radio broadcasts. One approach is to build an automatic speech recognition system for the target low-resource language. We compare this to using acoustic word embedding models that map speech segments to a space where matching words have similar vectors.
  arXiv  Detail & Related papers  (2023-06-01T07:25:10Z)
• Learning Cross-lingual Visual Speech Representations [108.68531445641769]
  Cross-lingual self-supervised visual representation learning has been a growing research topic in the last few years. We use the recently-proposed Raw Audio-Visual Speechs (RAVEn) framework to pre-train an audio-visual model with unlabelled data. Our experiments show that: (1) multi-lingual models with more data outperform monolingual ones, but, when keeping the amount of data fixed, monolingual models tend to reach better performance.
  arXiv  Detail & Related papers  (2023-03-14T17:05:08Z)
• Residual Information in Deep Speaker Embedding Architectures [4.619541348328938]
  This paper introduces an analysis over six sets of speaker embeddings extracted with some of the most recent and high-performing DNN architectures. The dataset includes 46 speakers uttering the same set of prompts, recorded in either a professional studio or their home environments. The results show that the discriminative power of the analyzed embeddings is very high, yet across all the analyzed architectures, residual information is still present in the representations.
  arXiv  Detail & Related papers  (2023-02-06T12:37:57Z)
• Self-Supervised Speech Representation Learning: A Review [105.1545308184483]
  Self-supervised representation learning methods promise a single universal model that would benefit a wide variety of tasks and domains. Speech representation learning is experiencing similar progress in three main categories: generative, contrastive, and predictive methods. This review presents approaches for self-supervised speech representation learning and their connection to other research areas.
  arXiv  Detail & Related papers  (2022-05-21T16:52:57Z)
• Automatic Dialect Density Estimation for African American English [74.44807604000967]
  We explore automatic prediction of dialect density of the African American English (AAE) dialect. dialect density is defined as the percentage of words in an utterance that contain characteristics of the non-standard dialect. We show a significant correlation between our predicted and ground truth dialect density measures for AAE speech in this database.
  arXiv  Detail & Related papers  (2022-04-03T01:34:48Z)
• Unsupervised Acoustic Unit Discovery by Leveraging a Language-Independent Subword Discriminative Feature Representation [31.87235700253597]
  This paper tackles automatically discovering phone-like acoustic units (AUD) from unlabeled speech data. We propose a two-stage approach: the first stage learns a subword-discriminative feature representation and the second stage applies clustering to the learned representation and obtains phone-like clusters as the discovered acoustic units.
  arXiv  Detail & Related papers  (2021-04-02T11:43:07Z)
• Unsupervised Pattern Discovery from Thematic Speech Archives Based on Multilingual Bottleneck Features [41.951988293049205]
  We propose a two-stage approach, which comprises unsupervised acoustic modeling and decoding, followed by pattern mining in acoustic unit sequences. The proposed system is able to effectively extract topic-related words and phrases from the lecture recordings on MIT OpenCourseWare.
  arXiv  Detail & Related papers  (2020-11-03T20:06:48Z)
• 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)
• Unsupervised Cross-Modal Audio Representation Learning from Unstructured Multilingual Text [69.55642178336953]
  We present an approach to unsupervised audio representation learning. Based on a triplet neural network architecture, we harnesses semantically related cross-modal information to estimate audio track-relatedness. We show that our approach is invariant to the variety of annotation styles as well as to the different languages of this collection.
  arXiv  Detail & Related papers  (2020-03-27T07:37:15Z)
• Continuous speech separation: dataset and analysis [52.10378896407332]
  In natural conversations, a speech signal is continuous, containing both overlapped and overlap-free components. This paper describes a dataset and protocols for evaluating continuous speech separation algorithms.
  arXiv  Detail & Related papers  (2020-01-30T18:01:31Z)

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.