Performance vs. hardware requirements in state-of-the-art automatic speech recognition

EURASIP Journal on Audio, Speech, and Music Processing

Michaela Blott¹  Alessandro Pappalardo¹  Alexandru-Lucian Georgescu²  Horia Cucu² 

[1] Research Labs, Xilinx, Dublin, Ireland;Speech and Dialogue Research Laboratory, University Politehnica of Bucharest, Bucharest, Romania;

关键词: Automatic speech recognition;    Survey;    End-to-end ASR systems;    Deep learning;    Performance analysis;   

DOI  :  10.1186/s13636-021-00217-4

来源: Springer

【 摘 要 】

The last decade brought significant advances in automatic speech recognition (ASR) thanks to the evolution of deep learning methods. ASR systems evolved from pipeline-based systems, that modeled hand-crafted speech features with probabilistic frameworks and generated phone posteriors, to end-to-end (E2E) systems, that translate the raw waveform directly into words using one deep neural network (DNN). The transcription accuracy greatly increased, leading to ASR technology being integrated into many commercial applications. However, few of the existing ASR technologies are suitable for integration in embedded applications, due to their hard constrains related to computing power and memory usage. This overview paper serves as a guided tour through the recent literature on speech recognition and compares the most popular ASR implementations. The comparison emphasizes the trade-off between ASR performance and hardware requirements, to further serve decision makers in choosing the system which fits best their embedded application. To the best of our knowledge, this is the first study to provide this kind of trade-off analysis for state-of-the-art ASR systems.

【 授权许可】

CC BY