Abstract
Speech classification has been widely used in many speech-related applications. However, the complexity of speech classification tasks often exceeds the scope of non-experts, the off-the-shelf speech classification methods are urgently needed. Recently, the automatic speech classification (AutoSpeech) without any human intervention has attracted more and more attention. The practical AutoSpeech solution should be general and can automatically handle classification tasks from different domains. Moreover, AutoSpeech should improve not only the final performance but also the any-time performance especially when the time budget is limited. To address these issues, we propose a three-stage any-time learning algorithm framework called Progressive AutoSpeech for automatic speech classification under a given time budget. Progressive AutoSpeech consists of the fast stage, enhancement stage, and exploration stage. Each stage uses different models and features to ensure generalization. Additionally, we automatically construct ensembles of top-k prediction results to improve the robustness. The experimental results reveal that Progressive AutoSpeech is effective and efficient for a wide range of speech classification tasks and can achieve the best ALC score.
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Notes
- 1.
Progressive AutoSpeech won the first place in the NeurIPS 2019 AutoSpeech challenge and the second place in the Interspeech 2020 AutoSpeech challenge.
References
Adavanne, S., Drossos, K., Çakir, E., Virtanen, T.: Stacked convolutional and recurrent neural networks for bird audio detection. In: Proceedings of the European Signal Processing Conference (EUSIPCO), pp. 1729–1733 (2017)
Adavanne, S., Virtanen, T.: Sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network. arXiv preprint arXiv:1710.02998 (2017)
Brazdil, P., Giraud-Carrier, C.: Metalearning and algorithm selection: progress, state of the art and introduction to the 2018 special issue. Mach. Learn. 107(1), 1–14 (2018)
Carmi, N., Cohen, A., Avigal, M., Lerner, A.: A storyteller’s tale: literature audiobooks genre classification using CNN and RNN architectures. In: Proceedings of Interspeech 2019, pp. 3387–3390 (2019)
Dai, W., Dai, C., Qu, S., Li, J., Das, S.: Very deep convolutional neural networks for raw waveforms. In: Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 421–425 (2017)
Ellis, D.P.W.: Classifying music audio with timbral and chroma features. In: Proceedings of the International Conference on Music Information Retrieval, pp. 339–340 (2007)
Ganchev, T., Fakotakis, N., Kokkinakis, G.: Comparative evaluation of various mfcc implementations on the speaker verification task. In: Proceedings of the International Conference on Speech and Computer, pp. 191–194 (2005)
Hutter, F., Kotthoff, L., Vanschoren, J. (eds.): Automated Machine Learning. TSSCML. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05318-5
Irvin, J., Chartock, E., Hollander, N.: Recurrent neural networks with attention for genre classification (2016)
Kim, T., Lee, J., Nam, J.: Comparison and analysis of sample cnn architectures for audio classification. IEEE J. Sel. Topics Signal Process. 13(2), 285–297 (2019)
Lin, Y.L., Wei, G.: Speech emotion recognition based on HMM and SVM. In: Proceedings of the International Conference on Machine Learning and Cybernetics, pp. 4898–4901 (2005)
Liu, C., Wang, Y., Kumar, K., Gong, Y.: Investigations on speaker adaptation of LSTM RNN models for speech recognition. In: Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5020–5024 (2016)
Liu, Z., et al.: Autocv challenge design and baseline results. In: CAp 2019 - Conférence sur l’Apprentissage Automatique. Toulouse, France (2019)
Majumder, N., Poria, S., Hazarika, D., Mihalcea, R., Gelbukh, A., Cambria, E.: Dialoguernn: an attentive RNN for emotion detection in conversations. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 6818–6825 (2019)
Malik, M., Adavanne, S., Drossos, K., Virtanen, T., Ticha, D., Jarina, R.: Stacked convolutional and recurrent neural networks for music emotion recognition. arXiv preprint arXiv:1706.02292 (2017)
Nagrani, A., Chung, J.S., Zisserman, A.: Voxceleb: a large-scale speaker identification dataset. arXiv preprint arXiv:1706.08612 (2017)
Nakagawa, S., Wang, L., Ohtsuka, S.: Speaker identification and verification by combining MFCC and phase information. IEEE Trans. Audio Speech Lang. Process. 20(4), 1085–1095 (2011)
Padi, B., Mohan, A., Ganapathy, S.: Attention based hybrid i-vector BLSTM model for language recognition. In: Proceedings of Interspeech 2019, pp. 1263–1267 (2019)
Panayotov, V., Chen, G., Povey, D., Khudanpur, S.: Librispeech: an ASR corpus based on public domain audio books. In: Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5206–5210 (2015)
Parchami, M., Zhu, W.P., Champagne, B., Plourde, E.: Recent developments in speech enhancement in the short-time fourier transform domain. IEEE Circ. Syst. Mag. 16(3), 45–77 (2016)
Park, K., Mulc, T.: Css10: a collection of single speaker speech datasets for 10 languages. arXiv preprint arXiv:1903.11269 (2019)
Reynolds, D.A., Quatieri, T.F., Dunn, R.B.: Speaker verification using adapted gaussian mixture models. Dig. Signal Process. 10(1–3), 19–41 (2000)
Shen, J., et al.: Natural TTS synthesis by conditioning wavenet on mel spectrogram predictions. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4779–4783. IEEE (2018)
Wang, J., et al.: Autospeech 2020: the second automated machine learning challenge for speech classification. In: Interspeech 2020, pp. 1967–1971 (2020)
Xie, W., Nagrani, A., Chung, J.S., Zisserman, A.: Utterance-level aggregation for speaker recognition in the wild. In: Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5791–5795 (2019)
Acknowledgments
This work was supported by the National Natural Science Foundation of China (U1811461), National Key R&D Program of China (2019YFC1711000), and Collaborative Innovation Center of Novel Software Technology and Industrialization.
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Zhu, G. et al. (2021). Progressive AutoSpeech: An Efficient and General Framework for Automatic Speech Classification. In: Karlapalem, K., et al. Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12713. Springer, Cham. https://doi.org/10.1007/978-3-030-75765-6_14
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