Text Augmentation for Language Models in High Error Recognition Scenario

Beneš, Karel; Burget, Lukáš

doi:10.21437/interspeech.2021-627

Cited by 3 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rosenberg et al [24] also train a Tacotron model on Librispeech corpus and out-of-domain speech to explore the effect of acoustic diversity. Text augmentation has been used [25,26] to improve language model scores and reduce WER. The aforementioned research has been adapted for languages with sufficient resources.…”

Section: Data Augmentation In Asrmentioning

confidence: 99%

Low Resource Kannada Speech Recognition using Lattice Rescoring and Speech Synthesis

Murthy¹,

Sitaram²

2023

IJST

View full text Add to dashboard Cite

Objectives: Improving the accuracy of low resource speech recognition in a model trained on only 4 hours of transcribed continuous speech in Kannada language, using data augmentation. Methods: Baseline language model is augmented with unigram counts of words, that are present in the Wikipedia text corpus but absent in the baseline, for initial decoding. Lattice rescoring is then applied using the language model augmented with Wikipedia text. Speech synthesis-based augmentation with multi-speaker syllable-based synthesis, using voices in Kannada and cross-lingual Telugu languages, is employed. We synthesize basic syllables, syllables with consonant conjuncts, and words that contain syllables that are absent in the training speech, for Kannada language. Findings: An overall word error rate (WER) of 9.04% is achieved over a baseline WER of 40.93%. Language model augmentation and lattice rescoring gives an absolute improvement of 16.68%. Applying our method of syllable-based speech synthesis over language model augmentation and rescoring yields a total reduction of 31.89% in WER. The proposed approach of language model augmentation is memory efficient and consumes only 1/8 th the memory required for decoding with Wikipedia augmented language model (2 gigabytes versus 18 gigabytes) while giving comparable WER (22.95% for Wikipedia versus 24.25% for our method). Augmentation with synthesized syllables enhances the ability of the speech recognition model to recognize basic sounds thus improving recognition of out-of-vocabulary words to 90% and in-vocabulary words to 97%. Novelty: We propose novel methods of language model augmentation and synthesis-based augmentation to achieve low WER for a speech recognition model trained on only 4 hours of continuous speech. Obtaining high recognition accuracy (or low WER) for very small speech corpus is a challenge. In this paper, we demonstrate that high accuracy can be achieved using data augmentation for a small corpus-based speech recognition.

show abstract

Section: Data Augmentation In Asrmentioning

confidence: 99%