Abstract
The main task of automatic speech recognition (ASR) is to convert voice signals to text transcriptions. It is one of the most important research fields in natural language processing (NLP). With more than a half century of endeavor, the word error rate (WER), which is a metric unit for transcription performance, has significantly been reduced. Particularly in recent years, due to the increase of computational power, large quantity of collected data, and efficient neural learning algorithms, the dominant power of deep learning technology further enhanced the performance of ASR systems to a practical level. However, there are still many issues that need to be further investigated for these systems to be adapted to a wide range of applications. In this chapter, we will introduce the main stream and pipeline of ASR frameworks, particularly the two dominant frameworks, i.e., Hidden Markov Model (HMM) with Gaussian Mixture model (GMM)-based ASR which dominated the field in the early decades, and deep learning model-based ASR which dominates the techniques used now. In addition, noisy robustness, which is one of the most important challenges for ASR in real applications, will also be introduced.
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Notes
- 1.
- 2.
SPINE: Speech in noisy environments, http://www.speech.sri.com/projects/spine/.
- 3.
Aurora speech recognition experimental framework, http://aurora.hsnr.de/index-2.html.
- 4.
Computational hearing in multisource environments (CHiME) challenge, http://spandh.dcs.shef.ac.uk/projects/chime/.
- 5.
Reverberant voice enhancement and recognition benchmark (REVERB) challenge, https://reverb2014.dereverberation.com/.
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Lu, X., Li, S., Fujimoto, M. (2020). Automatic Speech Recognition. In: Kidawara, Y., Sumita, E., Kawai, H. (eds) Speech-to-Speech Translation. SpringerBriefs in Computer Science. Springer, Singapore. https://doi.org/10.1007/978-981-15-0595-9_2
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