AUTHOR=Kumalija Elhard , Nakamoto Yukikazu TITLE=Performance evaluation of automatic speech recognition systems on integrated noise-network distorted speech JOURNAL=Frontiers in Signal Processing VOLUME=2 YEAR=2022 URL=https://www.frontiersin.org/journals/signal-processing/articles/10.3389/frsip.2022.999457 DOI=10.3389/frsip.2022.999457 ISSN=2673-8198 ABSTRACT=

In VoIP applications, such as Interactive Voice Response and VoIP-phone conversation transcription, speech signals are degraded not only by environmental noise but also by transmission network quality, and distortions induced by encoding and decoding algorithms. Therefore, there is a need for automatic speech recognition (ASR) systems to handle integrated noise-network distorted speech. In this study, we present a comparative analysis of a speech-to-text system trained on clean speech against one trained on integrated noise-network distorted speech. Training an ASR model on noise-network distorted speech dataset improves its robustness. Although the performance of an ASR model trained on clean speech depends on noise type, this is not the case when noise is further distorted by network transmission. The model trained on noise-network distorted speech exhibited a 60% improvement rate in the word error rate (WER), word match rate (MER), and word information lost (WIL) over the model trained on clean speech. Furthermore, the ASR model trained with noise-network distorted speech could tolerate a jitter of less than 20% and a packet loss of less than 15%, without a decrease in performance. However, WER, MER, and WIL increased in proportion to the jitter and packet loss as they exceeded 20% and 15%, respectively. Additionally, the model trained on noise-network distorted speech exhibited higher robustness compared to that trained on clean speech. The ASR model trained on noise-network distorted speech can also tolerate signal-to-noise (SNR) values of 5 dB and above, without the loss of performance, independent of noise type.