r-G2P: Evaluating and Enhancing Robustness of Grapheme to Phoneme Conversion by Controlled noise introducing and Contextual information incorporation

• URL: http://arxiv.org/abs/2202.11194v1
• Date: Mon, 21 Feb 2022 13:29:30 GMT
• Title: r-G2P: Evaluating and Enhancing Robustness of Grapheme to Phoneme Conversion by Controlled noise introducing and Contextual information incorporation
• Authors: Chendong Zhao, Jianzong Wang, Xiaoyang Qu, Haoqian Wang, Jing Xiao
• Abstract summary: We show that neural G2P models are extremely sensitive to orthographical variations in graphemes like spelling mistakes. We propose three controlled noise introducing methods to synthesize noisy training data. We incorporate the contextual information with the baseline and propose a robust training strategy to stabilize the training process.
• Score: 32.75866643254402
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Grapheme-to-phoneme (G2P) conversion is the process of converting the written form of words to their pronunciations. It has an important role for text-to-speech (TTS) synthesis and automatic speech recognition (ASR) systems. In this paper, we aim to evaluate and enhance the robustness of G2P models. We show that neural G2P models are extremely sensitive to orthographical variations in graphemes like spelling mistakes. To solve this problem, we propose three controlled noise introducing methods to synthesize noisy training data. Moreover, we incorporate the contextual information with the baseline and propose a robust training strategy to stabilize the training process. The experimental results demonstrate that our proposed robust G2P model (r-G2P) outperforms the baseline significantly (-2.73\% WER on Dict-based benchmarks and -9.09\% WER on Real-world sources).

Related papers

• Improving Grapheme-to-Phoneme Conversion through In-Context Knowledge Retrieval with Large Language Models [74.71484979138161]
  Grapheme-to-phoneme (G2P) conversion is a crucial step in Text-to-Speech (TTS) systems. Inspired by the success of Large Language Models (LLMs) in handling context-aware scenarios, contextual G2P conversion systems are proposed. The efficacy of incorporating ICKR into G2P conversion systems is demonstrated thoroughly on the Librig2p dataset.
  arXiv  Detail & Related papers  (2024-11-12T05:38:43Z)
• Speech collage: code-switched audio generation by collaging monolingual corpora [50.356820349870986]
  Speech Collage is a method that synthesizes CS data from monolingual corpora by splicing audio segments. We investigate the impact of generated data on speech recognition in two scenarios.
  arXiv  Detail & Related papers  (2023-09-27T14:17:53Z)
• ALIP: Adaptive Language-Image Pre-training with Synthetic Caption [78.93535202851278]
  Contrastive Language-Image Pre-training (CLIP) has significantly boosted the performance of various vision-language tasks. The presence of intrinsic noise and unmatched image-text pairs in web data can potentially affect the performance of representation learning. We propose an Adaptive Language-Image Pre-training (ALIP), a bi-path model that integrates supervision from both raw text and synthetic caption.
  arXiv  Detail & Related papers  (2023-08-16T15:19:52Z)
• Improving grapheme-to-phoneme conversion by learning pronunciations from speech recordings [12.669655363646257]
  The Grapheme-to-Phoneme (G2P) task aims to convert orthographic input into a discrete phonetic representation. We propose a method to improve the G2P conversion task by learning pronunciation examples from audio recordings.
  arXiv  Detail & Related papers  (2023-07-31T13:25:38Z)
• SoundChoice: Grapheme-to-Phoneme Models with Semantic Disambiguation [10.016862617549991]
  This paper proposes SoundChoice, a novel Grapheme-to-Phoneme (G2P) architecture that processes entire sentences rather than operating at the word level. SoundChoice achieves a Phoneme Error Rate (PER) of 2.65% on whole-sentence transcription using data from LibriSpeech and Wikipedia.
  arXiv  Detail & Related papers  (2022-07-27T01:14:59Z)
• SVTS: Scalable Video-to-Speech Synthesis [105.29009019733803]
  We introduce a scalable video-to-speech framework consisting of two components: a video-to-spectrogram predictor and a pre-trained neural vocoder. We are the first to show intelligible results on the challenging LRS3 dataset.
  arXiv  Detail & Related papers  (2022-05-04T13:34:07Z)
• Wav2vec-Switch: Contrastive Learning from Original-noisy Speech Pairs for Robust Speech Recognition [52.71604809100364]
  We propose wav2vec-Switch, a method to encode noise robustness into contextualized representations of speech. Specifically, we feed original-noisy speech pairs simultaneously into the wav2vec 2.0 network. In addition to the existing contrastive learning task, we switch the quantized representations of the original and noisy speech as additional prediction targets.
  arXiv  Detail & Related papers  (2021-10-11T00:08:48Z)
• Voice2Series: Reprogramming Acoustic Models for Time Series Classification [65.94154001167608]
  Voice2Series is a novel end-to-end approach that reprograms acoustic models for time series classification. We show that V2S either outperforms or is tied with state-of-the-art methods on 20 tasks, and improves their average accuracy by 1.84%.
  arXiv  Detail & Related papers  (2021-06-17T07:59:15Z)
• One Model to Pronounce Them All: Multilingual Grapheme-to-Phoneme Conversion With a Transformer Ensemble [0.0]
  We describe a simple approach of exploiting model ensembles, based on multilingual Transformers and self-training, to develop a highly effective G2P solution for 15 languages. Our best models achieve 14.99 word error rate (WER) and 3.30 phoneme error rate (PER), a sizeable improvement over the shared task competitive baselines.
  arXiv  Detail & Related papers  (2020-06-23T21:28:28Z)
• Transformer based Grapheme-to-Phoneme Conversion [0.9023847175654603]
  In this paper, we investigate the application of transformer architecture to G2P conversion. We compare its performance with recurrent and convolutional neural network based approaches. The results show that transformer based G2P outperforms the convolutional-based approach in terms of word error rate.
  arXiv  Detail & Related papers  (2020-04-14T07:48:15Z)

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.