An Investigation into the Effectiveness of Enhancement in ASR Training and Test for Chime-5 Dinner Party Transcription

By Catalin Zorila, Christoph Boeddeker, Rama Doddipatla, Reinhold Häb-Umbach. 2019

38 citations recorded by the source corpus.

Despite the strong modeling power of neural network acoustic models, speech enhancement has been shown to deliver additional word error rate improvements if multi-channel data is available. However, there has been a longstanding debate whether enhancement should also be carried out on the ASR training data. In an extensive experimental evaluation on the acoustically very challenging CHiME-5 dinner party data we show that: (i) cleaning up the training data can lead to substantial error rate reductions, and (ii) enhancement in training is advisable as long as enhancement in test is at least as strong as in training. This approach stands in contrast and delivers larger gains than the common strategy reported in the literature to augment the training database with additional artificially degraded speech. Together with an acoustic model topology consisting of initial CNN layers followed by factorized TDNN layers we achieve with 41.6 % and 43.2 % WER on the DEV and EVAL test sets, respectively, a new single-system state-of-the-art result on the CHiME-5 data. This is a 8 % relative improvement compared to the best word error rate published so far for a speech recognizer without system combination.

Subfields: Artificial Intelligence, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Signal Processing

Citations in the corpus

Real Time Speech Enhancement in the Waveform Domain (2020, background): “Thus, speech enhancement is a particularly important task in itself for audio and video calls [1], hearing aids [2], and can also help automatic speech recognition (ASR) systems [3].”
The STC System for the CHiME-6 Challenge (2020, methodology): “As in [19], this enhancement was also applied for training data augmentation and provided a significant WER reduction.”
End-to-End Speaker Diarization for an Unknown Number of Speakers with Encoder-Decoder Based Attractors (2020, background): “Accurate diarization has been proven to improve ASR performance by constraining a speech mask when constructing a beamformer for speech separation [6, 7].”
Towards a Competitive End-to-End Speech Recognition for CHiME-6 Dinner Party Transcription (2020, methodology): “• The use of GSS-based speech enhancement for training and testing data [24, 25].”