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Text Generation with Speech Synthesis for ASR Data Augmentation

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arxiv 2305.16333 v1 pith:DGXHKVVT submitted 2023-05-22 cs.CL cs.AIcs.LGeess.AS

Zhuangqun Huang , Gil Keren , Ziran Jiang , Shashank Jain , David Goss-Grubbs , Nelson Cheng , Farnaz Abtahi , Duc Le
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David Zhang Antony D'Avirro Ethan Campbell-Taylor Jessie Salas Irina-Elena Veliche Xi Chen
This is my paper · ORCID
classification cs.CLcs.AIcs.LGeess.AS
keywords augmentationtextdataspeechgenerationmethodsneuralsynthetic
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Aiming at reducing the reliance on expensive human annotations, data synthesis for Automatic Speech Recognition (ASR) has remained an active area of research. While prior work mainly focuses on synthetic speech generation for ASR data augmentation, its combination with text generation methods is considerably less explored. In this work, we explore text augmentation for ASR using large-scale pre-trained neural networks, and systematically compare those to traditional text augmentation methods. The generated synthetic texts are then converted to synthetic speech using a text-to-speech (TTS) system and added to the ASR training data. In experiments conducted on three datasets, we find that neural models achieve 9%-15% relative WER improvement and outperform traditional methods. We conclude that text augmentation, particularly through modern neural approaches, is a viable tool for improving the accuracy of ASR systems.

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Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Data Augmentation for Spoken Grammatical Error Correction

    cs.CL 2025-07 conditional novelty 6.0 of 10

    A fully automated pipeline generates audio-text pairs with grammar errors and disfluencies for spoken grammatical error correction, with four objective metrics to select the best generated data.

  2. Recognizing Every Voice: Towards Inclusive ASR for Rural Bhojpuri Women

    eess.AS 2025-06 conditional novelty 5.0 of 10

    Using 25-30 seconds of audio per speaker from 100 rural Bhojpuri women, synthetic speech augmentation cuts ASR word error on the new SRUTI benchmark by 4.7 points.

  3. A Domain Adaptation Framework for Speech Recognition Systems with Only Synthetic data

    eess.AS 2025-01 conditional novelty 5.0 of 10

    DAS adapts Whisper to new domains using only LLM-generated text and TTS speech, with per-domain LoRA adapters and a confidence-based multi-LoRA decoding rule, improving WER by 10-17% with about 1% out-of-domain regression.

  4. WebSight: A Vision-First Architecture for Robust Web Agents

    cs.AI 2025-08 reject novelty 4.0 of 10

    A vision-only web agent with a LoRA-fine-tuned 7B model and multi-agent planning achieves 68% success on a filtered WebVoyager subset and 58.84% top-1 click accuracy on Showdown Clicks.

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