Comparing the Benefit of Synthetic Training Data for Various Automatic Speech Recognition Architectures

IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), pp. 788–795

Abstract

Recent publications on automatic-speech-recognition (ASR) have a strong focus on attention encoder-decoder (AED) architectures which tend to suffer from over-fitting in low resource scenarios. One solution to tackle this issue is to generate synthetic data with a trained text-to-speech system (TTS) if additional text is available. This was successfully applied in many publications with AED systems, but only very limited in the context of other ASR architectures. We investigate the effect of varying pre-processing, the speaker embedding and input encoding of the TTS system w.r.t. the effectiveness of the synthesized data for AED-ASR training. Additionally, we also consider internal language model subtraction for the first time, resulting in up to 38% relative improvement. We compare the AED results to a state-of-the-art hybrid ASR system, a monophone based system using connectionist-temporal-classification (CTC) and a monotonic transducer based system. We show that for the later systems the addition of synthetic data has no relevant effect, but they still outperform the AED systems on LibriSpeech-100h. We achieve a final word-error-rate of 3.3%/10.0% with a hybrid system on the clean/noisy test-sets, surpassing any previous state-of-the-art systems on Librispeech-100h that do not include unlabeled audio data.

Authors 5

1. Nick Rossenbach Aachen Human Language Technology and Pattern Recognition

   RWTH Aachen University

   Affiliation as printed

   Human Language Technology and Pattern Recognition, RWTH Aachen University,Computer Science Department,Aachen,Germany,52074

2. Mohammad Zeineldeen Aachen Human Language Technology and Pattern Recognition

   RWTH Aachen University

   Affiliation as printed

   Human Language Technology and Pattern Recognition, RWTH Aachen University,Computer Science Department,Aachen,Germany,52074

3. Benedikt Hilmes Aachen Human Language Technology and Pattern Recognition

   RWTH Aachen University

   Affiliation as printed

   Human Language Technology and Pattern Recognition, RWTH Aachen University,Computer Science Department,Aachen,Germany,52074

4. Ralf Schlüter Aachen Human Language Technology and Pattern Recognition

   RWTH Aachen University

   Affiliation as printed

   Human Language Technology and Pattern Recognition, RWTH Aachen University,Computer Science Department,Aachen,Germany,52074

5. Hermann Ney Aachen Human Language Technology and Pattern Recognition

   RWTH Aachen University

   Affiliation as printed

   Human Language Technology and Pattern Recognition, RWTH Aachen University,Computer Science Department,Aachen,Germany,52074