Abstract
The automatic recognition of MP3 compressed speech presents a challenge to the current systems due to the lossy nature of compression which causes irreversible degradation of the speech wave. This article evaluates the performance of a recognition system optimized for MP3 compressed speech with current state-of-the-art acoustic modelling techniques and one specific front-end compensation method. The article concentrates on acoustic model adaptation, discriminative training, and additional dithering as prominent means of compensating for the described distortion in the task of phoneme and large vocabulary continuous speech recognition (LVCSR). The experiments presented on the phoneme task show a dramatic increase of the recognition error for unvoiced speech units as a direct result of compression. The application of acoustic model adaptation has proved to yield the highest relative contribution while the gain of discriminative training diminished with decreasing bit-rate. The application of additional dithering yielded a consistent improvement only for the MFCC features, but the overall results were still worse than those for the PLP features.
Highlights
The aim of automatic speech recognition (ASR) research is to develop an intermediary system for the purpose of human speech transcription where the construction and block architecture is often customized
The results were evaluated by phone error rate (PER) and the phone error rate reduction (PERR) criteria: PER = S + D + I × 100 [ %], N
Its application was useful for baseline and Linear discriminant analysis (LDA) models, but the reduction for more advanced acoustic modelling techniques was only marginal and higher bit-rates were mainly unaffected by the method
Summary
The aim of automatic speech recognition (ASR) research is to develop an intermediary system for the purpose of human speech transcription where the construction and block architecture is often customized. This article investigates the performance of current state-of-the-art acoustic modelling (AM) and feature extraction techniques in the task of phoneme and large vocabulary continuous speech recognition of MP3 compressed speech.
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