Semi-supervised acoustic modelling for five-lingual code-switched ASR using automatically-segmented soap opera speech
| Autor: | Wilkinson, N., Biswas, A., Yılmaz, E., de Wet, F., van der Westhuizen, E., Niesler, T. R. |
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| Rok vydání: | 2020 |
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| Druh dokumentu: | Working Paper |
| Popis: | This paper considers the impact of automatic segmentation on the fully-automatic, semi-supervised training of automatic speech recognition (ASR) systems for five-lingual code-switched (CS) speech. Four automatic segmentation techniques were evaluated in terms of the recognition performance of an ASR system trained on the resulting segments in a semi-supervised manner. The system's output was compared with the recognition rates achieved by a semi-supervised system trained on manually assigned segments. Three of the automatic techniques use a newly proposed convolutional neural network (CNN) model for framewise classification, and include a novel form of HMM smoothing of the CNN outputs. Automatic segmentation was applied in combination with automatic speaker diarization. The best-performing segmentation technique was also tested without speaker diarization. An evaluation based on 248 unsegmented soap opera episodes indicated that voice activity detection (VAD) based on a CNN followed by Gaussian mixture modelhidden Markov model smoothing (CNN-GMM-HMM) yields the best ASR performance. The semi-supervised system trained with the resulting segments achieved an overall WER improvement of 1.1% absolute over the system trained with manually created segments. Furthermore, we found that system performance improved even further when the automatic segmentation was used in conjunction with speaker diarization. Comment: SLTU 2020. arXiv admin note: text overlap with arXiv:2003.03135 |
| Databáze: | arXiv |
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