You Do Not Need More Data: Improving End-To-End Speech Recognition by Text-To-Speech Data Augmentation

• URL: http://arxiv.org/abs/2005.07157v2
• Date: Thu, 30 Jul 2020 20:26:57 GMT
• Title: You Do Not Need More Data: Improving End-To-End Speech Recognition by Text-To-Speech Data Augmentation
• Authors: Aleksandr Laptev, Roman Korostik, Aleksey Svischev, Andrei Andrusenko, Ivan Medennikov, Sergey Rybin
• Abstract summary: We build our TTS system on an ASR training database and then extend the data with synthesized speech to train a recognition model. Our system establishes a competitive result for end-to-end ASR trained on LibriSpeech train-clean-100 set with WER 4.3% for test-clean and 13.5% for test-other.
• Score: 59.31769998728787
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data augmentation is one of the most effective ways to make end-to-end automatic speech recognition (ASR) perform close to the conventional hybrid approach, especially when dealing with low-resource tasks. Using recent advances in speech synthesis (text-to-speech, or TTS), we build our TTS system on an ASR training database and then extend the data with synthesized speech to train a recognition model. We argue that, when the training data amount is relatively low, this approach can allow an end-to-end model to reach hybrid systems' quality. For an artificial low-to-medium-resource setup, we compare the proposed augmentation with the semi-supervised learning technique. We also investigate the influence of vocoder usage on final ASR performance by comparing Griffin-Lim algorithm with our modified LPCNet. When applied with an external language model, our approach outperforms a semi-supervised setup for LibriSpeech test-clean and only 33% worse than a comparable supervised setup. Our system establishes a competitive result for end-to-end ASR trained on LibriSpeech train-clean-100 set with WER 4.3% for test-clean and 13.5% for test-other.

Related papers

• Improving End-to-End Speech Processing by Efficient Text Data Utilization with Latent Synthesis [17.604583337593677]
  Training a high performance end-to-end speech (E2E) processing model requires an enormous amount of labeled speech data. We propose Latent Synthesis (LaSyn), an efficient textual data utilization framework for E2E speech processing models.
  arXiv  Detail & Related papers  (2023-10-09T03:10:49Z)
• Towards Selection of Text-to-speech Data to Augment ASR Training [20.115236045164355]
  We train a neural network to measure the similarity of a synthetic data to real speech. We find that incorporating synthetic samples with considerable dissimilarity to real speech is crucial for boosting recognition performance.
  arXiv  Detail & Related papers  (2023-05-30T17:24:28Z)
• Using External Off-Policy Speech-To-Text Mappings in Contextual End-To-End Automated Speech Recognition [19.489794740679024]
  We investigate the potential of leveraging external knowledge, particularly through off-policy key-value stores generated with text-to-speech methods. In our approach, audio embeddings captured from text-to-speech, along with semantic text embeddings, are used to bias ASR. Experiments on LibiriSpeech and in-house voice assistant/search datasets show that the proposed approach can reduce domain adaptation time by up to 1K GPU-hours.
  arXiv  Detail & Related papers  (2023-01-06T22:32:50Z)
• Audio-Visual Efficient Conformer for Robust Speech Recognition [91.3755431537592]
  We propose to improve the noise of the recently proposed Efficient Conformer Connectionist Temporal Classification architecture by processing both audio and visual modalities. Our experiments show that using audio and visual modalities allows to better recognize speech in the presence of environmental noise and significantly accelerate training, reaching lower WER with 4 times less training steps.
  arXiv  Detail & Related papers  (2023-01-04T05:36:56Z)
• Continual Learning for On-Device Speech Recognition using Disentangled Conformers [54.32320258055716]
  We introduce a continual learning benchmark for speaker-specific domain adaptation derived from LibriVox audiobooks. We propose a novel compute-efficient continual learning algorithm called DisentangledCL. Our experiments show that the DisConformer models significantly outperform baselines on general ASR.
  arXiv  Detail & Related papers  (2022-12-02T18:58:51Z)
• Enhanced Direct Speech-to-Speech Translation Using Self-supervised Pre-training and Data Augmentation [76.13334392868208]
  Direct speech-to-speech translation (S2ST) models suffer from data scarcity issues. In this work, we explore self-supervised pre-training with unlabeled speech data and data augmentation to tackle this issue.
  arXiv  Detail & Related papers  (2022-04-06T17:59:22Z)
• Comparing the Benefit of Synthetic Training Data for Various Automatic Speech Recognition Architectures [44.803590841664]
  We present a novel approach of silence correction in the data pre-processing for TTS systems. We achieve a final word-error-rate of 3.3%/10.0% with a Hybrid system on the clean/noisy test-sets.
  arXiv  Detail & Related papers  (2021-04-12T11:59:23Z)
• 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)
• MixSpeech: Data Augmentation for Low-resource Automatic Speech Recognition [54.84624870942339]
  MixSpeech is a simple yet effective data augmentation method based on mixup for automatic speech recognition (ASR) We apply MixSpeech on two popular end-to-end speech recognition models including LAS (Listen, Attend and Spell) and Transformer. Experimental results show that MixSpeech achieves better accuracy than the baseline models without data augmentation.
  arXiv  Detail & Related papers  (2021-02-25T03:40:43Z)

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.