This paper investigates speaker adaptation techniques for bidirectional long short term memory (BLSTM) recurrent neural network based acoustic models (AMs) trained with the connectionist temporal classification (CTC) objective function. BLSTM-CTC AMs play an important role in end-to-end automatic speech recognition systems. However, there is a lack of research in speaker adaptation algorithms for these models. We explore three different feature-space adaptation approaches for CTC AMs: feature-space maximum linear regression, i-vector based adaptation, and maximum a posteriori adaptation using GMM-derived features. Experimental results on the TED-LIUM corpus demonstrate that speaker adaptation, applied in combination with data augmentation techniques, provides, in an unsupervised adaptation mode, for different test sets, up to 11–20% of relative word error rate reduction over the baseline model built on the raw filter-bank features. In addition, the adaptation behavior is compared for BLSTM-CTC AMs and time-delay neural network AMs trained with the cross-entropy criterion.
@InProceedings{TOMASHENKO18.881, author = {Natalia Tomashenko and Yannick Estève}, title = "{Evaluation of Feature-Space Speaker Adaptation for End-to-End Acoustic Models}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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