Synt++: Utilizing Imperfect Synthetic Data to Improve Speech Recognition Article Swipe
Ting-Yao Hu
,
Mohammadreza Armandpour
,
Ashish Shrivastava
,
Jen-Hao Rick Chang
,
Hema Swetha Koppula
,
Oncel Tuzel
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2110.11479
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2110.11479
With recent advances in speech synthesis, synthetic data is becoming a viable alternative to real data for training speech recognition models. However, machine learning with synthetic data is not trivial due to the gap between the synthetic and the real data distributions. Synthetic datasets may contain artifacts that do not exist in real data such as structured noise, content errors, or unrealistic speaking styles. Moreover, the synthesis process may introduce a bias due to uneven sampling of the data manifold. We propose two novel techniques during training to mitigate the problems due to the distribution gap: (i) a rejection sampling algorithm and (ii) using separate batch normalization statistics for the real and the synthetic samples. We show that these methods significantly improve the training of speech recognition models using synthetic data. We evaluate the proposed approach on keyword detection and Automatic Speech Recognition (ASR) tasks, and observe up to 18% and 13% relative error reduction, respectively, compared to naively using the synthetic data.
Related Topics
Concepts
Synthetic data
Computer science
Normalization (sociology)
Speech recognition
Speech synthesis
Artificial intelligence
Sampling (signal processing)
Pattern recognition (psychology)
Noise reduction
Noise (video)
Training set
Machine learning
Computer vision
Filter (signal processing)
Image (mathematics)
Sociology
Anthropology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2110.11479
- https://arxiv.org/pdf/2110.11479
- OA Status
- green
- Cited By
- 4
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4300426785
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4300426785Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2110.11479Digital Object Identifier
- Title
-
Synt++: Utilizing Imperfect Synthetic Data to Improve Speech RecognitionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-10-21Full publication date if available
- Authors
-
Ting-Yao Hu, Mohammadreza Armandpour, Ashish Shrivastava, Jen-Hao Rick Chang, Hema Swetha Koppula, Oncel TuzelList of authors in order
- Landing page
-
https://arxiv.org/abs/2110.11479Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2110.11479Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2110.11479Direct OA link when available
- Concepts
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Synthetic data, Computer science, Normalization (sociology), Speech recognition, Speech synthesis, Artificial intelligence, Sampling (signal processing), Pattern recognition (psychology), Noise reduction, Noise (video), Training set, Machine learning, Computer vision, Filter (signal processing), Image (mathematics), Sociology, AnthropologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 3, 2022: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.datasets | 43 |
| abstract_inverted_index.evaluate | 132 |
| abstract_inverted_index.learning | 23 |
| abstract_inverted_index.mitigate | 88 |
| abstract_inverted_index.problems | 90 |
| abstract_inverted_index.proposed | 134 |
| abstract_inverted_index.relative | 152 |
| abstract_inverted_index.samples. | 114 |
| abstract_inverted_index.sampling | 75, 99 |
| abstract_inverted_index.separate | 104 |
| abstract_inverted_index.speaking | 62 |
| abstract_inverted_index.training | 17, 86, 123 |
| abstract_inverted_index.Automatic | 140 |
| abstract_inverted_index.Moreover, | 64 |
| abstract_inverted_index.Synthetic | 42 |
| abstract_inverted_index.algorithm | 100 |
| abstract_inverted_index.artifacts | 46 |
| abstract_inverted_index.detection | 138 |
| abstract_inverted_index.introduce | 69 |
| abstract_inverted_index.manifold. | 79 |
| abstract_inverted_index.rejection | 98 |
| abstract_inverted_index.synthesis | 66 |
| abstract_inverted_index.synthetic | 6, 25, 36, 113, 129, 161 |
| abstract_inverted_index.reduction, | 154 |
| abstract_inverted_index.statistics | 107 |
| abstract_inverted_index.structured | 56 |
| abstract_inverted_index.synthesis, | 5 |
| abstract_inverted_index.techniques | 84 |
| abstract_inverted_index.Recognition | 142 |
| abstract_inverted_index.alternative | 12 |
| abstract_inverted_index.recognition | 19, 126 |
| abstract_inverted_index.unrealistic | 61 |
| abstract_inverted_index.distribution | 94 |
| abstract_inverted_index.normalization | 106 |
| abstract_inverted_index.respectively, | 155 |
| abstract_inverted_index.significantly | 120 |
| abstract_inverted_index.distributions. | 41 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.5699999928474426 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |