Language-agnostic, automated assessment of listeners' speech recall using large language models

• URL: http://arxiv.org/abs/2503.01045v1
• Date: Sun, 02 Mar 2025 22:28:41 GMT
• Title: Language-agnostic, automated assessment of listeners' speech recall using large language models
• Authors: Björn Herrmann,
• Abstract summary: This research leverages modern large language models (LLMs) in native English speakers and native speakers of 10 other languages.<n>Participants listened to and freely recalled short stories (in quiet/clear and in babble noise) in their native language.<n>LLMs prompt engineering with semantic similarity analyses to score speech recall revealed sensitivity to known effects of temporal order, primacy/recency, and background noise.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Speech-comprehension difficulties are common among older people. Standard speech tests do not fully capture such difficulties because the tests poorly resemble the context-rich, story-like nature of ongoing conversation and are typically available only in a country's dominant/official language (e.g., English), leading to inaccurate scores for native speakers of other languages. Assessments for naturalistic, story speech in multiple languages require accurate, time-efficient scoring. The current research leverages modern large language models (LLMs) in native English speakers and native speakers of 10 other languages to automate the generation of high-quality, spoken stories and scoring of speech recall in different languages. Participants listened to and freely recalled short stories (in quiet/clear and in babble noise) in their native language. LLM text-embeddings and LLM prompt engineering with semantic similarity analyses to score speech recall revealed sensitivity to known effects of temporal order, primacy/recency, and background noise, and high similarity of recall scores across languages. The work overcomes limitations associated with simple speech materials and testing of closed native-speaker groups because recall data of varying length and details can be mapped across languages with high accuracy. The full automation of speech generation and recall scoring provides an important step towards comprehension assessments of naturalistic speech with clinical applicability.

Related papers

• Long-Form Speech Generation with Spoken Language Models [64.29591880693468]
  SpeechSSM learns from and sample long-form spoken audio in a single decoding session without text intermediates.<n>New embedding-based and LLM-judged metrics; quality measurements over length and time; and a new benchmark for long-form speech processing and generation, LibriSpeech-Long.
  arXiv  Detail & Related papers  (2024-12-24T18:56:46Z)
• Quantifying the Dialect Gap and its Correlates Across Languages [69.18461982439031]
  This work will lay the foundation for furthering the field of dialectal NLP by laying out evident disparities and identifying possible pathways for addressing them through mindful data collection.
  arXiv  Detail & Related papers  (2023-10-23T17:42:01Z)
• CLARA: Multilingual Contrastive Learning for Audio Representation Acquisition [5.520654376217889]
  CLARA minimizes reliance on labelled data, enhancing generalization across languages. Our approach adeptly captures emotional nuances in speech, overcoming subjective assessment issues. It adapts to low-resource languages, marking progress in multilingual speech representation learning.
  arXiv  Detail & Related papers  (2023-10-18T09:31:56Z)
• Can Language Models Learn to Listen? [96.01685069483025]
  We present a framework for generating appropriate facial responses from a listener in dyadic social interactions based on the speaker's words. Our approach autoregressively predicts a response of a listener: a sequence of listener facial gestures, quantized using a VQ-VAE. We show that our generated listener motion is fluent and reflective of language semantics through quantitative metrics and a qualitative user study.
  arXiv  Detail & Related papers  (2023-08-21T17:59:02Z)
• 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)
• Speak Foreign Languages with Your Own Voice: Cross-Lingual Neural Codec Language Modeling [92.55131711064935]
  We propose a cross-lingual neural language model, VALL-E X, for cross-lingual speech synthesis. VALL-E X inherits strong in-context learning capabilities and can be applied for zero-shot cross-lingual text-to-speech synthesis and zero-shot speech-to-speech translation tasks. It can generate high-quality speech in the target language via just one speech utterance in the source language as a prompt while preserving the unseen speaker's voice, emotion, and acoustic environment.
  arXiv  Detail & Related papers  (2023-03-07T14:31:55Z)
• M-SpeechCLIP: Leveraging Large-Scale, Pre-Trained Models for Multilingual Speech to Image Retrieval [56.49878599920353]
  This work investigates the use of large-scale, English-only pre-trained models (CLIP and HuBERT) for multilingual image-speech retrieval. For non-English image-speech retrieval, we outperform the current state-of-the-art performance by a wide margin both when training separate models for each language, and with a single model which processes speech in all three languages.
  arXiv  Detail & Related papers  (2022-11-02T14:54:45Z)
• Automatic Spoken Language Identification using a Time-Delay Neural Network [0.0]
  A language identification system was built to distinguish between Arabic, Spanish, French, and Turkish. A pre-existing multilingual dataset was used to train a series of acoustic models. The system was provided with a custom multilingual language model and a specialized pronunciation lexicon.
  arXiv  Detail & Related papers  (2022-05-19T13:47:48Z)
• Zero-Shot Cross-lingual Aphasia Detection using Automatic Speech Recognition [3.2631198264090746]
  Aphasia is a common speech and language disorder, typically caused by a brain injury or a stroke, that affects millions of people worldwide. We propose an end-to-end pipeline using pre-trained Automatic Speech Recognition (ASR) models that share cross-lingual speech representations.
  arXiv  Detail & Related papers  (2022-04-01T14:05:02Z)
• Cross-lingual Low Resource Speaker Adaptation Using Phonological Features [2.8080708404213373]
  We train a language-agnostic multispeaker model conditioned on a set of phonologically derived features common across different languages. With as few as 32 and 8 utterances of target speaker data, we obtain high speaker similarity scores and naturalness comparable to the corresponding literature.
  arXiv  Detail & Related papers  (2021-11-17T12:33:42Z)
• Towards Language Modelling in the Speech Domain Using Sub-word Linguistic Units [56.52704348773307]
  We propose a novel LSTM-based generative speech LM based on linguistic units including syllables and phonemes. With a limited dataset, orders of magnitude smaller than that required by contemporary generative models, our model closely approximates babbling speech. We show the effect of training with auxiliary text LMs, multitask learning objectives, and auxiliary articulatory features.
  arXiv  Detail & Related papers  (2021-10-31T22:48:30Z)

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.