Enhancing Multilingual Voice Toxicity Detection with Speech-Text Alignment

• URL: http://arxiv.org/abs/2406.10325v1
• Date: Fri, 14 Jun 2024 17:56:53 GMT
• Title: Enhancing Multilingual Voice Toxicity Detection with Speech-Text Alignment
• Authors: Joseph Liu, Mahesh Kumar Nandwana, Janne Pylkkönen, Hannes Heikinheimo, Morgan McGuire,
• Abstract summary: Toxicity classification for voice heavily relies on semantic content of speech. We propose a novel framework that utilizes cross-modal learning to integrate the semantic embedding of text into a multilabel speech toxicity classifier.
• Score: 4.2936749846785345
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Toxicity classification for voice heavily relies on the semantic content of speech. We propose a novel framework that utilizes cross-modal learning to integrate the semantic embedding of text into a multilabel speech toxicity classifier during training. This enables us to incorporate textual information during training while still requiring only audio during inference. We evaluate this classifier on large-scale datasets with real-world characteristics to validate the effectiveness of this framework. Through ablation studies, we demonstrate that general-purpose semantic text embeddings are rich and aligned with speech for toxicity classification purposes. Conducting experiments across multiple languages at scale, we show improvements in voice toxicity classification across five languages and different toxicity categories.

Related papers

• Paralinguistics-Enhanced Large Language Modeling of Spoken Dialogue [71.15186328127409]
  Paralinguistics-enhanced Generative Pretrained Transformer (ParalinGPT) Model takes the conversational context of text, speech embeddings, and paralinguistic attributes as input prompts within a serialized multitasking framework. We utilize the Switchboard-1 corpus, including its sentiment labels as the paralinguistic attribute, as our spoken dialogue dataset.
  arXiv  Detail & Related papers  (2023-12-23T18:14:56Z)
• Leveraging Language Model Capabilities for Sound Event Detection [10.792576135806623]
  We propose an end-to-end framework for understanding audio features while simultaneously generating sound event and their temporal location. Specifically, we employ pretrained acoustic models to capture discriminative features across different categories and language models for autoregressive text generation.
  arXiv  Detail & Related papers  (2023-08-22T15:59:06Z)
• ContextSpeech: Expressive and Efficient Text-to-Speech for Paragraph Reading [65.88161811719353]
  This work develops a lightweight yet effective Text-to-Speech system, ContextSpeech. We first design a memory-cached recurrence mechanism to incorporate global text and speech context into sentence encoding. We construct hierarchically-structured textual semantics to broaden the scope for global context enhancement. Experiments show that ContextSpeech significantly improves the voice quality and prosody in paragraph reading with competitive model efficiency.
  arXiv  Detail & Related papers  (2023-07-03T06:55:03Z)
• Unsupervised Improvement of Audio-Text Cross-Modal Representations [19.960695758478153]
  We study unsupervised approaches to improve the learning framework of such representations with unpaired text and audio. We show that when domain-specific curation is used in conjunction with a soft-labeled contrastive loss, we are able to obtain significant improvement in terms of zero-shot classification performance.
  arXiv  Detail & Related papers  (2023-05-03T02:30:46Z)
• What Have Been Learned & What Should Be Learned? An Empirical Study of How to Selectively Augment Text for Classification [0.0]
  We propose STA (Selective Text Augmentation) to selectively augment the text, where the informative, class-indicating words are emphasized but the irrelevant or noisy words are diminished. Experiments on four English and Chinese text classification benchmark datasets demonstrate that STA can substantially outperform the non-selective text augmentation methods.
  arXiv  Detail & Related papers  (2021-09-01T04:03:11Z)
• Deep Learning for Prominence Detection in Children's Read Speech [13.041607703862724]
  We consider a labeled dataset of children's reading recordings for the speaker-independent detection of prominent words. A previous well-tuned random forest ensemble predictor is replaced by an RNN sequence to exploit potential context dependency. Deep learning is applied to obtain word-level features from low-level acoustic contours of fundamental frequency, intensity and spectral shape.
  arXiv  Detail & Related papers  (2021-04-12T14:15:08Z)
• Leveraging Acoustic and Linguistic Embeddings from Pretrained speech and language Models for Intent Classification [81.80311855996584]
  We propose a novel intent classification framework that employs acoustic features extracted from a pretrained speech recognition system and linguistic features learned from a pretrained language model. We achieve 90.86% and 99.07% accuracy on ATIS and Fluent speech corpus, respectively.
  arXiv  Detail & Related papers  (2021-02-15T07:20:06Z)
• Leveraging Adversarial Training in Self-Learning for Cross-Lingual Text Classification [52.69730591919885]
  We present a semi-supervised adversarial training process that minimizes the maximal loss for label-preserving input perturbations. We observe significant gains in effectiveness on document and intent classification for a diverse set of languages.
  arXiv  Detail & Related papers  (2020-07-29T19:38:35Z)
• Speaker Diarization with Lexical Information [59.983797884955]
  This work presents a novel approach for speaker diarization to leverage lexical information provided by automatic speech recognition. We propose a speaker diarization system that can incorporate word-level speaker turn probabilities with speaker embeddings into a speaker clustering process to improve the overall diarization accuracy.
  arXiv  Detail & Related papers  (2020-04-13T17:16:56Z)
• 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)

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.