Summary Efficient Subset Selection for Self-Supervised Speech Recognition arxiv.org
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The COW-ERAGE method efficiently selects subsets in self-supervised speech recognition, outperforming other strategies by focusing on examples with moderate phoneme count and exploring phonemic diversity.
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Key Points
- The study focuses on efficient fine-tuning in self-supervised speech recognition models.
- The authors propose the COWERAGE algorithm for representative subset selection in self-supervised ASR.
- COWERAGE ensures the coverage of examples based on training Word Error Rate (WER) in the early training epochs.
- Extensive experiments show the effectiveness of COWERAGE in improving WER performance.
- COWERAGE leads to better test accuracy in self-supervised speech recognition models.
- The study analyzes the relationship between training WER and phonemic cover, highlighting the importance of phonemic diversity.
- COWERAGE consistently outperforms other subset selection strategies such as random selection and top k/bottom k.
- The study unveils the connection between training WER and phonemic cover, emphasizing the importance of phonemic diversity in improving performance.
Summaries
33 word summary
COW-ERAGE is a method for efficient subset selection in self-supervised speech recognition. It outperforms other pruning strategies and selects examples with moderate phoneme count. Phonemic diversity is explored and COW-ERAGE is found effective.
62 word summary
The study presents COW-ERAGE, a new method for efficient subset selection in self-supervised speech recognition models. COW-ERAGE prunes data based on training word error rate. It outperforms other pruning strategies by selecting examples with a moderate number of phonemes, resulting in lower WER. The study also explores phonemic diversity and compares COW-ERAGE with other methods, highlighting its effectiveness in selecting informative examples.
171 word summary
The study introduces a new method called COW-ERAGE for efficient subset selection in self-supervised speech recognition models. COW-ERAGE prunes data by sampling examples based on their training word error rate (WER). The effectiveness of COW-ERAGE is evaluated on the wav2vec2 and HuBERT models using three datasets. The authors analyze the phoneme distribution of training examples and its relationship with training error to understand why COW-ERAGE performs better than other pruning strategies. They find that examples with a moderate number of phonemes have a lower WER. Statistical analysis confirms the significant differences between the phoneme distributions of COW-ERAGE and other strategies. The study also explores how phonemic diversity impacts latent speech representations and compares COW-ERAGE with other subset selection strategies. The results demonstrate the effectiveness of COW-ERAGE in selecting informative and representative examples. The study also discusses related work and highlights the importance of phonemic diversity. In conclusion, COW-ERAGE is proposed as a new method for pruning data, which outperforms random selection and other strategies while highlighting the importance of phonemic diversity.
440 word summary
The study focuses on efficient subset selection for self-supervised speech recognition models. The authors propose a new method called COW-ERAGE that prunes data by sampling examples based on their training word error rate (WER). The effectiveness of COW-ERAGE is evaluated on the wav2vec2 and HuBERT models using three datasets: TIMIT, Librispeech 10h, and LJSpeech.
To understand why COW-ERAGE performs better than other pruning strategies, the authors analyze the phoneme distribution of training examples and its relationship with the training error. They find that examples with a low or high phonemic cover tend to have a higher WER in the earlier training epochs, while examples with a moderate number of phonemes have a lower WER. In the later epochs, the inverse relationship becomes more evident, with examples containing more distinct phonemes having a lower WER.
The authors conduct a statistical analysis to determine if the difference between the phoneme distributions of the examples within the COW-ERAGE subset and the other two strategies (top k and bottom k) is significant. They find that the differences are statistically significant at the 1% level.
The study also investigates how phonemic diversity impacts the discrete latent speech representations within self-supervised speech recognition models. The authors analyze the latent representation learned by the quantizer within wav2vec2 for different phonemes. They find that different discrete latents specialize in different phonetic sounds, supporting the hypothesis that greater phonemic diversity enables a more robust latent representation of each phoneme.
The authors compare COW-ERAGE with other subset selection strategies, including random selection, top k, and bottom k. They evaluate the performance of the subsets on wav2vec2 and HuBERT models using the TIMIT, Librispeech 10h, and LJSpeech datasets. The results show that COW-ERAGE consistently outperforms the other strategies, demonstrating its effectiveness in selecting informative and representative examples.
The study also discusses related work in active learning, data pruning, and data subset selection for ASR systems. It highlights the importance of phonemically rich text and higher coverage of words in existing approaches. The authors note that while model pruning has been explored for self-supervised and other ASR models, data subset selection for fine-tuning self-supervised ASR systems has only been explored in the context of personalization for accented speakers.
In conclusion, the authors propose COW-ERAGE as a new method for pruning data in self-supervised automatic speech recognition. The method selects examples based on their training WER to ensure coverage of training WER values. The evaluation on wav2vec2 and HuBERT models demonstrates that COW-ERAGE outperforms random selection and other data pruning strategies. The study also unveils the connection between training WER and phonemic cover and highlights the importance of phonemic diversity in improving performance.
511 word summary
The study focuses on efficient subset selection for self-supervised speech recognition models. The authors propose a new method called COW-ERAGE that prunes data by sampling examples based on their training word error rate (WER). The effectiveness of COW-ERAGE is evaluated on the wav2vec2 and HuBERT models using three datasets: TIMIT, Librispeech 10h, and LJSpeech.
To understand why COW-ERAGE performs better than other pruning strategies, the authors analyze the phoneme distribution of training examples and its relationship with the training error. They find that examples with a low or high phonemic cover tend to have a higher WER in the earlier training epochs, while examples with a moderate number of phonemes have a lower WER. In the later epochs, the inverse relationship becomes more evident, with examples containing more distinct phonemes having a lower WER.
The relationship between the training WER and the phonemic cover has several implications. It suggests that there is a population of sentences with a low phonemic cover that are harder to learn and represent a higher training WER. Conversely, there are many low WER sentences with a high phonemic cover. The authors validate their claim that ensuring the coverage of training WER values in a particular subset leads to improved performance.
The authors conduct a statistical analysis to determine if the difference between the phoneme distributions of the examples within the COW-ERAGE subset and the other two strategies (top k and bottom k) is significant. They find that the differences are statistically significant at the 1% level.
The study also investigates how phonemic diversity impacts the discrete latent speech representations within self-supervised speech recognition models. The authors analyze the latent representation learned by the quantizer within wav2vec2 for different phonemes. They find that different discrete latents specialize in different phonetic sounds, supporting the hypothesis that greater phonemic diversity enables a more robust latent representation of each phoneme.
The authors compare COW-ERAGE with other subset selection strategies, including random selection, top k, and bottom k. They evaluate the performance of the subsets on wav2vec2 and HuBERT models using the TIMIT, Librispeech 10h, and LJSpeech datasets. The results show that COW-ERAGE consistently outperforms the other strategies, demonstrating its effectiveness in selecting informative and representative examples.
The study also discusses related work in active learning, data pruning, and data subset selection for ASR systems. It highlights the importance of phonemically rich text and higher coverage of words in existing approaches. The authors note that while model pruning has been explored for self-supervised and other ASR models, data subset selection for fine-tuning self-supervised ASR systems has only been explored in the context of personalization for accented speakers.
In conclusion, the authors propose COW-ERAGE as a new method for pruning data in self-supervised automatic speech recognition. The method selects examples based on their training WER to ensure coverage of training WER values. The evaluation on wav2vec2 and HuBERT models demonstrates that COW-ERAGE outperforms random selection and other data pruning strategies. The study also unveils the connection between training WER and phonemic cover and highlights the importance of phonemic diversity in improving performance.
1139 word summary
The study focuses on efficient fine-tuning in self-supervised speech recognition models. Fine-tuning these models requires a significant amount of labeled training data, which can be computationally demanding and time-consuming. The authors explore the task of identifying an optimal subset of data for efficient fine-tuning in self-supervised speech models for automatic speech recognition (ASR). They find that dataset pruning strategies used in vision tasks do not perform better than random subset selection for fine-tuning self-supervised ASR.
To address this issue, the authors propose the COWERAGE algorithm for representative subset selection in self-supervised ASR. They discover that ensuring the coverage of examples based on training Word Error Rate (WER) in the early training epochs leads to better generalization performance. Extensive experiments with the wav2vec 2.0 and HuBERT models on TIMIT, Librispeech, and LJSpeech datasets show the effectiveness of COWERAGE and its transferability across models, with up to 17% relative WER improvement over existing dataset pruning methods and random sampling. The authors also demonstrate that the coverage of training instances in terms of WER values ensures the inclusion of phonemically diverse examples, leading to better test accuracy in self-supervised speech recognition models.
The study begins by highlighting the rapid progress in self-supervised speech learning models and the need for labeled training data in the fine-tuning step. The authors note that this requirement is computationally demanding and time-consuming, hindering the usage of these models in low-resource systems. They mention recent work that uses adapters to enable efficient fine-tuning but highlight their limitations in terms of applicability across different models and datasets.
The authors propose increasing the efficiency of speech SSL fine-tuning by reducing training data requirements and finding smaller, representative, and model-agnostic subsets of data for fine-tuning speech SSL models. They emphasize the importance of studying the impact of data subset selection on ASR model performance and raise several questions related to identifying a model-agnostic scoring method based on training properties for dataset pruning in speech SSL.
The authors discuss data pruning mechanisms specifically tailored for deep learning models and highlight the lack of such mechanisms for data pruning in speech SSL models. They propose the COWERAGE algorithm as a novel approach for dataset pruning in self-supervised ASR. COWERAGE ensures the coverage of diverse examples based on training WER values in the early training epochs, leading to better accuracy on unseen test data compared to random pruning or selecting only the most informative examples.
The authors present different strategies for subset selection, including picking the hardest k examples, picking the easiest k examples, and using the COWERAGE algorithm. They compare these strategies and find that COWERAGE consistently outperforms the others in terms of test WER. They also evaluate the impact of increasing the bucket size on test WER and find that larger bucket sizes lead to better performance.
The study includes empirical evaluation experiments using the wav2vec2-base and HuBERT-base models on TIMIT, Librispeech, and LJSpeech datasets. The results show that COWERAGE consistently demonstrates the lowest WER at various pruning fractions. The authors also demonstrate the transferability of representative subsets computed through COWERAGE from one speech SSL model to another, making them model-agnostic and dataset-specific.
Furthermore, the authors evaluate the impact of subset selection methods on phoneme recognition using the wav2vec2-base model on the TIMIT dataset. They find that COWERAGE consistently outperforms the other strategies in terms of phoneme error rate.
The study concludes by highlighting the practical implications of subset selection methods in terms of training time for subsets. The authors conduct an experiment to determine the total steps required for convergence and the real training time for wav2vec2 on TIMIT. They find a significant reduction in training time for higher pruning fractions.
In summary, the authors propose the COWERAGE algorithm for representative subset selection in self-supervised ASR. The algorithm ensures the
The study focuses on efficient subset selection for self-supervised speech recognition models. The authors propose a new method called COW-ERAGE that prunes data by sampling examples based on their training word error rate (WER). The effectiveness of COW-ERAGE is evaluated on the wav2vec2 and HuBERT models using three datasets: TIMIT, Librispeech 10h, and LJSpeech.
To understand why COW-ERAGE performs better than other pruning strategies, the authors analyze the phoneme distribution of training examples and its relationship with the training error. They find that examples with a low or high phonemic cover tend to have a higher WER in the earlier training epochs, while examples with a moderate number of phonemes have a lower WER. In the later epochs, the inverse relationship becomes more evident, with examples containing more distinct phonemes having a lower WER.
The relationship between the training WER and the phonemic cover has several implications. It suggests that there is a population of sentences with a low phonemic cover that are harder to learn and represent a higher training WER. Conversely, there are many low WER sentences with a high phonemic cover. The authors validate their claim that ensuring the coverage of training WER values in a particular subset leads to improved performance.
The authors conduct a statistical analysis to determine if the difference between the phoneme distributions of the examples within the COW-ERAGE subset and the other two strategies (top k and bottom k) is significant. They find that the differences are statistically significant at the 1% level.
The study also investigates how phonemic diversity impacts the discrete latent speech representations within self-supervised speech recognition models. The authors analyze the latent representation learned by the quantizer within wav2vec2 for different phonemes. They find that different discrete latents specialize in different phonetic sounds, supporting the hypothesis that greater phonemic diversity enables a more robust latent representation of each phoneme.
The authors compare COW-ERAGE with other subset selection strategies, including random selection, top k, and bottom k. They evaluate the performance of the subsets on wav2vec2 and HuBERT models using the TIMIT, Librispeech 10h, and LJSpeech datasets. The results show that COW-ERAGE consistently outperforms the other strategies, demonstrating its effectiveness in selecting informative and representative examples.
The study also discusses related work in active learning, data pruning, and data subset selection for ASR systems. It highlights the importance of phonemically rich text and higher coverage of words in existing approaches. The authors note that while model pruning has been explored for self-supervised and other ASR models, data subset selection for fine-tuning self-supervised ASR systems has only been explored in the context of personalization for accented speakers.
In conclusion, the authors propose COW-ERAGE as a new method for pruning data in self-supervised automatic speech recognition. The method selects examples based on their training WER to ensure coverage of training WER values. The evaluation on wav2vec2 and HuBERT models demonstrates that COW-ERAGE outperforms random selection and other data pruning strategies. The study also unveils the connection between training WER and phonemic cover and highlights the importance of phonemic diversity in improving performance.