CUPE: Contextless Universal Phoneme Encoder for Language-Agnostic Speech Processing

• URL: http://arxiv.org/abs/2508.15316v1
• Date: Thu, 21 Aug 2025 07:27:10 GMT
• Title: CUPE: Contextless Universal Phoneme Encoder for Language-Agnostic Speech Processing
• Authors: Abdul Rehman, Jian-Jun Zhang, Xiaosong Yang,
• Abstract summary: CUPE is a lightweight model that captures key phoneme features in just 120 milliseconds.<n> CUPE achieves competitive cross-lingual performance by learning fundamental acoustic patterns common to all languages.
• Score: 5.466034990848432
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Universal phoneme recognition typically requires analyzing long speech segments and language-specific patterns. Many speech processing tasks require pure phoneme representations free from contextual influence, which motivated our development of CUPE - a lightweight model that captures key phoneme features in just 120 milliseconds, about one phoneme's length. CUPE processes short, fixed-width windows independently and, despite fewer parameters than current approaches, achieves competitive cross-lingual performance by learning fundamental acoustic patterns common to all languages. Our extensive evaluation through supervised and self-supervised training on diverse languages, including zero-shot tests on the UCLA Phonetic Corpus, demonstrates strong cross-lingual generalization and reveals that effective universal speech processing is possible through modeling basic acoustic patterns within phoneme-length windows.

Related papers

• PRiSM: Benchmarking Phone Realization in Speech Models [70.82595415252682]
  Phone recognition (PR) serves as the atomic interface for language-agnostic modeling for cross-lingual speech processing and phonetic analysis.<n>We introduce PRiSM, the first open-source benchmark designed to expose blind spots in phonetic perception.
  arXiv  Detail & Related papers  (2026-01-20T15:00:36Z)
• Do Audio-Language Models Understand Linguistic Variations? [42.17718387132912]
  Open-vocabulary audio language models (ALMs) represent a promising new paradigm for audio-text retrieval using natural language queries.<n>We propose RobustCLAP, a novel and compute-efficient technique to learn audio-language representations to linguistic variations.
  arXiv  Detail & Related papers  (2024-10-21T20:55:33Z)
• Language-Universal Speech Attributes Modeling for Zero-Shot Multilingual Spoken Keyword Recognition [26.693942793501204]
  We propose a novel language-universal approach to end-to-end automatic spoken keyword recognition (SKR) Wav2Vec2.0 is used to generate robust speech representations, followed by a linear output layer to produce attribute sequences. A non-trainable pronunciation model then maps sequences of attributes into spoken keywords in a multilingual setting.
  arXiv  Detail & Related papers  (2024-06-04T16:59:11Z)
• The taste of IPA: Towards open-vocabulary keyword spotting and forced alignment in any language [7.0944623704102625]
  We show that phoneme-based models for speech processing can achieve strong crosslinguistic generalizability to unseen languages. We propose CLAP-IPA, a multi-lingual phoneme-speech contrastive embedding model capable of open-vocabulary matching between arbitrary speech signals and phonemic sequences.
  arXiv  Detail & Related papers  (2023-11-14T17:09:07Z)
• AudioPaLM: A Large Language Model That Can Speak and Listen [79.44757696533709]
  We introduce AudioPaLM, a large language model for speech understanding and generation. AudioPaLM fuses text-based and speech-based language models. It can process and generate text and speech with applications including speech recognition and speech-to-speech translation.
  arXiv  Detail & Related papers  (2023-06-22T14:37:54Z)
• 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)
• ERNIE-SAT: Speech and Text Joint Pretraining for Cross-Lingual Multi-Speaker Text-to-Speech [58.93395189153713]
  We extend the pretraining method for cross-lingual multi-speaker speech synthesis tasks. We propose a speech-text joint pretraining framework, where we randomly mask the spectrogram and the phonemes. Our model shows great improvements over speaker-embedding-based multi-speaker TTS methods.
  arXiv  Detail & Related papers  (2022-11-07T13:35:16Z)
• 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)
• Common Phone: A Multilingual Dataset for Robust Acoustic Modelling [13.930464898816652]
  This work introduces Common Phone, a gender-balanced, multilingual corpus recorded from more than 76.000 contributors via Mozilla's Common Voice project. It comprises around 116 hours of speech enriched with automatically generated phonetic segmentation. A Wav2Vec 2.0 acoustic model was trained with the Common Phone to perform phonetic symbol recognition and validate the quality of the generated phonetic annotation.
  arXiv  Detail & Related papers  (2022-01-15T19:02:46Z)
• Differentiable Allophone Graphs for Language-Universal Speech Recognition [77.2981317283029]
  Building language-universal speech recognition systems entails producing phonological units of spoken sound that can be shared across languages. We present a general framework to derive phone-level supervision from only phonemic transcriptions and phone-to-phoneme mappings. We build a universal phone-based speech recognition model with interpretable probabilistic phone-to-phoneme mappings for each language.
  arXiv  Detail & Related papers  (2021-07-24T15:09:32Z)
• Towards Natural and Controllable Cross-Lingual Voice Conversion Based on Neural TTS Model and Phonetic Posteriorgram [21.652906261475533]
  Cross-lingual voice conversion is a challenging problem due to significant mismatches of the phonetic set and the speech prosody of different languages. We build upon the neural text-to-speech (TTS) model to design a new cross-lingual VC framework named FastSpeech-VC.
  arXiv  Detail & Related papers  (2021-02-03T10:28:07Z)
• Universal Phone Recognition with a Multilingual Allophone System [135.2254086165086]
  We propose a joint model of language-independent phone and language-dependent phoneme distributions. In multilingual ASR experiments over 11 languages, we find that this model improves testing performance by 2% phoneme error rate absolute. Our recognizer achieves phone accuracy improvements of more than 17%, moving a step closer to speech recognition for all languages in the world.
  arXiv  Detail & Related papers  (2020-02-26T21:28:57Z)

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.