Goodness-of-pronunciation without phoneme time alignment

• URL: http://arxiv.org/abs/2603.25150v1
• Date: Thu, 26 Mar 2026 08:12:19 GMT
• Title: Goodness-of-pronunciation without phoneme time alignment
• Abstract summary: Open-source weakly-supervised models are capable of ASR over many languages, but they are frame-asynchronous and not phonemic.<n>This paper proposes to overcome incompatibilities for feature extraction with weakly-supervised models.<n>Phoneme posteriors are computed by mapping ASR hypotheses to a phoneme confusion network.
• Score: 40.19694209745764
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In speech evaluation, an Automatic Speech Recognition (ASR) model often computes time boundaries and phoneme posteriors for input features. However, limited data for ASR training hinders expansion of speech evaluation to low-resource languages. Open-source weakly-supervised models are capable of ASR over many languages, but they are frame-asynchronous and not phonemic, hindering feature extraction for speech evaluation. This paper proposes to overcome incompatibilities for feature extraction with weakly-supervised models, easing expansion of speech evaluation to low-resource languages. Phoneme posteriors are computed by mapping ASR hypotheses to a phoneme confusion network. Word instead of phoneme-level speaking rate and duration are used. Phoneme and frame-level features are combined using a cross-attention architecture, obviating phoneme time alignment. This performs comparably with standard frame-synchronous features on English speechocean762 and low-resource Tamil datasets.

Related papers

• Syllabic-Structure Decoder for Automatic Speech Recognition in Vietnamese [8.447993256993428]
  We are motivated from the phonemic features of Vietnamese to propose a Syllabic-Structure Decoder for ASR.<n>Our approach explicitly captures the phonological composition of syllables, enabling the decoder to generate valid syllabic structures from a compact phonemic inventory.
  arXiv  Detail & Related papers  (2026-05-27T02:51:09Z)
• Hard to Be Heard: Phoneme-Level ASR Analysis of Phonologically Complex, Low-Resource Endangered Languages [2.3513645401551337]
  We present a phoneme-level analysis of automatic speech recognition (ASR) for two low-resourced and phonologically complex East Caucasian languages.<n>Existing recordings and transcriptions are consolidated and processed into a form suitable for ASR training and evaluation.
  arXiv  Detail & Related papers  (2026-04-20T12:54:14Z)
• In-Sync: Adaptation of Speech Aware Large Language Models for ASR with Word Level Timestamp Predictions [51.236148875603185]
  We extend an existing speech-aware language model to predict timestamps directly alongside transcripts.<n>We introduce a set of novel lightweight training strategies that improve alignment robustness while preserving recognition quality.
  arXiv  Detail & Related papers  (2026-04-14T20:56:24Z)
• 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)
• WESR: Scaling and Evaluating Word-level Event-Speech Recognition [59.21814194620928]
  Speech conveys not only linguistic information but also rich non-verbal vocal events such as laughing and crying.<n>We develop a refined taxonomy of 21 vocal events, with a new categorization into discrete (standalone) versus continuous (mixed with speech) types.<n>Based on the refined taxonomy, we introduce WESR-Bench, an expert-annotated evaluation set (900+ utterances) with a novel position-aware protocol.
  arXiv  Detail & Related papers  (2026-01-08T02:23:21Z)
• ASR Benchmarking: Need for a More Representative Conversational Dataset [3.017953715883516]
  We introduce a multilingual conversational dataset, derived from TalkBank, consisting of unstructured phone conversation between adults. Our results show a significant performance drop across various state-of-the-art ASR models when tested in conversational settings.
  arXiv  Detail & Related papers  (2024-09-18T15:03:04Z)
• Towards Unsupervised Speech Recognition Without Pronunciation Models [57.222729245842054]
  In this article, we tackle the challenge of developing ASR systems without paired speech and text corpora.<n>We experimentally demonstrate that an unsupervised speech recognizer can emerge from joint speech-to-speech and text-to-text masked token-infilling.<n>This innovative model surpasses the performance of previous unsupervised ASR models under the lexicon-free setting.
  arXiv  Detail & Related papers  (2024-06-12T16:30:58Z)
• Dynamic Acoustic Unit Augmentation With BPE-Dropout for Low-Resource End-to-End Speech Recognition [62.94773371761236]
  We consider building an effective end-to-end ASR system in low-resource setups with a high OOV rate. We propose a method of dynamic acoustic unit augmentation based on the BPE-dropout technique. Our monolingual Turkish Conformer established a competitive result with 22.2% character error rate (CER) and 38.9% word error rate (WER)
  arXiv  Detail & Related papers  (2021-03-12T10:10:13Z)
• Phoneme-BERT: Joint Language Modelling of Phoneme Sequence and ASR Transcript [6.696983725360809]
  PhonemeBERT learns a joint language model with phoneme sequence and ASR transcript to learn phonetic-aware representations. We evaluate our approach extensively by generating noisy data for three benchmark datasets.
  arXiv  Detail & Related papers  (2021-02-01T12:45:15Z)
• Context-Dependent Acoustic Modeling without Explicit Phone Clustering [45.07737874541304]
  Phoneme-based acoustic modeling of large vocabulary automatic speech recognition takes advantage of phoneme context. In this work, we address a direct phonetic context modeling for the hybrid deep neural network (DNN)/HMM. By performing different decompositions of the joint probability of the center phoneme state and its left and right contexts, we obtain a factorized network consisting of different components.
  arXiv  Detail & Related papers  (2020-05-15T14:45:32Z)

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.